Lunar_DQN_Final.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import gym\n",
    "from gym.wrappers import RecordVideo\n",
    "import numpy as np\n",
    "import random\n",
    "from collections import deque\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import os \n",
    "os.environ['KMP_DUPLICATE_LIB_OK']='True'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "RANDOM_SEED = 42\n",
    "random.seed(RANDOM_SEED)\n",
    "np.random.seed(RANDOM_SEED)\n",
    "torch.manual_seed(RANDOM_SEED)\n",
    "if torch.cuda.is_available():\n",
    "    torch.cuda.manual_seed_all(RANDOM_SEED)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Network\n",
    "class DQN(nn.Module):\n",
    "    def __init__(self, input_size, output_size, hidden_size=64):\n",
    "        super(DQN, self).__init__()\n",
    "        self.fc1 = nn.Linear(input_size, hidden_size)\n",
    "        self.relu1 = nn.ReLU(inplace=True)\n",
    "        self.fc2 = nn.Linear(hidden_size, hidden_size)\n",
    "        self.relu2 = nn.ReLU(inplace=True)\n",
    "        self.fc3 = nn.Linear(hidden_size, output_size)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.fc1(x)\n",
    "        x = self.relu1(x)\n",
    "        x = self.fc2(x)\n",
    "        x = self.relu2(x)\n",
    "        x = self.fc3(x)\n",
    "        return x\n",
    "\n",
    "# Agent\n",
    "class Agent:\n",
    "    def __init__(self):\n",
    "        self.q_network = DQN(input_size, output_size).to(device)\n",
    "        self.target_network = DQN(input_size, output_size).to(device)\n",
    "        self.target_network.load_state_dict(self.q_network.state_dict())\n",
    "        self.optimizer = optim.Adam(self.q_network.parameters(), lr=lr)\n",
    "        self.replay_buffer = Memory(memory_size)\n",
    "\n",
    "    def q_updation(self, gamma, size):\n",
    "        if len(self.replay_buffer) < size:\n",
    "            return\n",
    "        \n",
    "        batch = self.replay_buffer.get(size)\n",
    "        states, actions, rewards, next_states, dones = batch\n",
    "\n",
    "        states = torch.as_tensor(states, dtype=torch.float32, device=device)\n",
    "        next_states = torch.as_tensor(next_states, dtype=torch.float32, device=device)\n",
    "        actions = torch.as_tensor(actions, dtype=torch.int64, device=device).unsqueeze(-1)\n",
    "        rewards = torch.as_tensor(rewards, dtype=torch.float32, device=device)\n",
    "        dones = torch.as_tensor(dones, dtype=torch.bool, device=device)\n",
    "\n",
    "        current_q_values = self.q_network(states).gather(1, actions)\n",
    "\n",
    "        with torch.no_grad():\n",
    "            next_q_values = self.target_network(next_states).max(1)[0]\n",
    "            expected_q_values = rewards + gamma * next_q_values * (~dones)\n",
    "\n",
    "        loss = nn.functional.mse_loss(current_q_values.squeeze(-1), expected_q_values)\n",
    "\n",
    "        self.optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        self.optimizer.step()\n",
    "\n",
    "    def get_action(self, state, epsilon):\n",
    "        if random.random() > epsilon:\n",
    "            state_tensor = torch.as_tensor(state, dtype=torch.float32, device=device).unsqueeze(0)\n",
    "            with torch.no_grad():\n",
    "                q_values = self.q_network(state_tensor)\n",
    "                action = torch.argmax(q_values, dim=1).item()\n",
    "        else:\n",
    "            action = random.randint(0, output_size - 1)\n",
    "\n",
    "        return action\n",
    "\n",
    "\n",
    "\n",
    "class Memory:\n",
    "    def __init__(self, size):\n",
    "        self.memory = deque(maxlen=size)\n",
    "\n",
    "    def add(self, state, action, reward, next_state, done):\n",
    "        self.memory.append((state, action, reward, next_state, done))\n",
    "\n",
    "    def get(self, batch):\n",
    "        state, action, reward, next_state, done = zip(*random.sample(self.memory, batch))\n",
    "        return np.array(state), np.array(action), np.array(reward), np.array(next_state), np.array(done)\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.memory)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "background_save": true,
     "base_uri": "https://localhost:8080/"
    },
    "id": "oGidxBPYFuVF",
    "outputId": "ca8f482b-0a0b-4e7b-a356-f62769131a10",
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\gym\\core.py:172: DeprecationWarning: \u001b[33mWARN: Function `env.seed(seed)` is marked as deprecated and will be removed in the future. Please use `env.reset(seed=seed) instead.\u001b[0m\n",
      "  deprecation(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Episode: 0, Total reward: -122.94725570383171, Average Rewards: -122.94725570383171, Epsilon: 1.00, Learning_rate: 0.0003\n",
      "Episode: 1, Total reward: -113.2494200549402, Average Rewards: -118.09833787938595, Epsilon: 1.00, Learning_rate: 0.0003\n",
      "Episode: 2, Total reward: -75.4740819622, Average Rewards: -103.89025257365729, Epsilon: 1.00, Learning_rate: 0.0003\n",
      "Episode: 3, Total reward: -246.1905414749282, Average Rewards: -139.46532479897502, Epsilon: 1.00, Learning_rate: 0.0003\n",
      "Episode: 4, Total reward: -151.08920641817423, Average Rewards: -141.79010112281486, Epsilon: 1.00, Learning_rate: 0.0003\n",
      "Episode: 5, Total reward: -280.3612818370011, Average Rewards: -164.88529790851257, Epsilon: 1.00, Learning_rate: 0.0003\n",
      "Episode: 6, Total reward: -347.1504506505289, Average Rewards: -190.92317687165774, Epsilon: 1.00, Learning_rate: 0.0003\n",
      "Episode: 7, Total reward: -88.49802227671016, Average Rewards: -178.12003254728933, Epsilon: 0.99, Learning_rate: 0.0003\n",
      "Episode: 8, Total reward: -125.96837281173393, Average Rewards: -172.32540368778317, Epsilon: 0.99, Learning_rate: 0.0003\n",
      "Episode: 9, Total reward: -173.43030584532733, Average Rewards: -172.4358939035376, Epsilon: 0.99, Learning_rate: 0.0003\n",
      "Episode: 10, Total reward: -196.3829624561004, Average Rewards: -174.61290013558875, Epsilon: 0.99, Learning_rate: 0.0003\n",
      "Episode: 11, Total reward: -91.31852636858062, Average Rewards: -167.6717023216714, Epsilon: 0.99, Learning_rate: 0.0003\n",
      "Episode: 12, Total reward: -89.4731670080497, Average Rewards: -161.65643037446975, Epsilon: 0.99, Learning_rate: 0.0003\n",
      "Episode: 13, Total reward: -90.40022201092772, Average Rewards: -156.56670120564533, Epsilon: 0.99, Learning_rate: 0.0003\n",
      "Episode: 14, Total reward: -264.42168277669964, Average Rewards: -163.75703331038227, Epsilon: 0.99, Learning_rate: 0.0003\n",
      "Episode: 15, Total reward: -348.4912861597548, Average Rewards: -175.30292411346804, Epsilon: 0.99, Learning_rate: 0.0003\n",
      "Episode: 16, Total reward: -122.44569125533656, Average Rewards: -172.19367512181324, Epsilon: 0.99, Learning_rate: 0.0003\n",
      "Episode: 17, Total reward: -88.39951498820179, Average Rewards: -167.53844400327927, Epsilon: 0.99, Learning_rate: 0.0003\n",
      "Episode: 18, Total reward: -214.56583065727403, Average Rewards: -170.0135696166474, Epsilon: 0.99, Learning_rate: 0.0003\n",
      "Episode: 19, Total reward: -125.8626163473481, Average Rewards: -167.80602195318244, Epsilon: 0.99, Learning_rate: 0.0003\n",
      "Episode: 20, Total reward: -116.78528094317386, Average Rewards: -165.37646285746774, Epsilon: 0.99, Learning_rate: 0.0003\n",
      "Episode: 21, Total reward: -173.95501595801335, Average Rewards: -165.76639708931074, Epsilon: 0.99, Learning_rate: 0.0003\n",
      "Episode: 22, Total reward: -78.06682284382386, Average Rewards: -161.95337212211567, Epsilon: 0.99, Learning_rate: 0.0003\n",
      "Episode: 23, Total reward: -119.08968474098934, Average Rewards: -160.16738514790208, Epsilon: 0.99, Learning_rate: 0.0003\n",
      "Episode: 24, Total reward: -108.31825658966578, Average Rewards: -158.09342000557263, Epsilon: 0.98, Learning_rate: 0.0003\n",
      "Episode: 25, Total reward: -190.84637862178164, Average Rewards: -159.35314918311911, Epsilon: 0.98, Learning_rate: 0.0003\n",
      "Episode: 26, Total reward: -107.83520915070193, Average Rewards: -157.44507733006665, Epsilon: 0.98, Learning_rate: 0.0003\n",
      "Episode: 27, Total reward: -86.36765582051095, Average Rewards: -154.90659799043965, Epsilon: 0.98, Learning_rate: 0.0003\n",
      "Episode: 28, Total reward: -117.35075096799304, Average Rewards: -153.61156878276907, Epsilon: 0.98, Learning_rate: 0.0003\n",
      "Episode: 29, Total reward: -156.80426737900086, Average Rewards: -153.71799206931013, Epsilon: 0.98, Learning_rate: 0.0003\n",
      "Episode: 30, Total reward: -110.79342401324693, Average Rewards: -152.33332858363067, Epsilon: 0.98, Learning_rate: 0.0003\n",
      "Episode: 31, Total reward: -251.34411329063155, Average Rewards: -155.42741560572443, Epsilon: 0.98, Learning_rate: 0.0003\n",
      "Episode: 32, Total reward: -6.870660893949804, Average Rewards: -150.9256957659737, Epsilon: 0.98, Learning_rate: 0.0003\n",
      "Episode: 33, Total reward: -200.16795720127033, Average Rewards: -152.37399757289418, Epsilon: 0.98, Learning_rate: 0.0003\n",
      "Episode: 34, Total reward: -62.49862546691436, Average Rewards: -149.8061297984376, Epsilon: 0.98, Learning_rate: 0.0003\n",
      "Episode: 35, Total reward: -235.69369571194377, Average Rewards: -152.19189551825724, Epsilon: 0.98, Learning_rate: 0.0003\n",
      "Episode: 36, Total reward: -103.59086271913178, Average Rewards: -150.87835409125387, Epsilon: 0.98, Learning_rate: 0.0003\n",
      "Episode: 37, Total reward: -199.23677526678142, Average Rewards: -152.1509441221888, Epsilon: 0.98, Learning_rate: 0.0003\n",
      "Episode: 38, Total reward: -278.46762161709563, Average Rewards: -155.38983328872487, Epsilon: 0.98, Learning_rate: 0.0003\n",
      "Episode: 39, Total reward: -319.745057780892, Average Rewards: -159.49871390102902, Epsilon: 0.98, Learning_rate: 0.0003\n",
      "Episode: 40, Total reward: -123.18891821189754, Average Rewards: -158.6131091281234, Epsilon: 0.97, Learning_rate: 0.0003\n",
      "Episode: 41, Total reward: -68.11943889784092, Average Rewards: -156.45849793216428, Epsilon: 0.97, Learning_rate: 0.0003\n",
      "Episode: 42, Total reward: -248.73186666719891, Average Rewards: -158.60439022832787, Epsilon: 0.97, Learning_rate: 0.0003\n",
      "Episode: 43, Total reward: -91.38167138627358, Average Rewards: -157.07660116373575, Epsilon: 0.97, Learning_rate: 0.0003\n",
      "Episode: 44, Total reward: -111.71433333508318, Average Rewards: -156.06855076754346, Epsilon: 0.97, Learning_rate: 0.0003\n",
      "Episode: 45, Total reward: -246.49381406429694, Average Rewards: -158.03431736095115, Epsilon: 0.97, Learning_rate: 0.0003\n",
      "Episode: 46, Total reward: -308.5281436026967, Average Rewards: -161.23631366396702, Epsilon: 0.97, Learning_rate: 0.0003\n",
      "Episode: 47, Total reward: -174.92227504938717, Average Rewards: -161.5214378594966, Epsilon: 0.97, Learning_rate: 0.0003\n",
      "Episode: 48, Total reward: -102.06049023838322, Average Rewards: -160.30794913253507, Epsilon: 0.97, Learning_rate: 0.0003\n",
      "Episode: 49, Total reward: -152.00782782183452, Average Rewards: -160.14194670632108, Epsilon: 0.97, Learning_rate: 0.0003\n",
      "Episode: 50, Total reward: -107.42528140738587, Average Rewards: -159.10828660242038, Epsilon: 0.97, Learning_rate: 0.0003\n",
      "Episode: 51, Total reward: -83.41019156547851, Average Rewards: -157.65255400555614, Epsilon: 0.97, Learning_rate: 0.0003\n",
      "Episode: 52, Total reward: -79.59734538490426, Average Rewards: -156.1798142202608, Epsilon: 0.97, Learning_rate: 0.0003\n",
      "Episode: 53, Total reward: -109.70421649088696, Average Rewards: -155.31915500305018, Epsilon: 0.97, Learning_rate: 0.0003\n",
      "Episode: 54, Total reward: -341.8402048869731, Average Rewards: -158.71044681912153, Epsilon: 0.97, Learning_rate: 0.0003\n",
      "Episode: 55, Total reward: -191.9486147026513, Average Rewards: -159.3039855313274, Epsilon: 0.97, Learning_rate: 0.0003\n",
      "Episode: 56, Total reward: -125.65043550141144, Average Rewards: -158.71357237290783, Epsilon: 0.96, Learning_rate: 0.0003\n",
      "Episode: 57, Total reward: -165.57443619170496, Average Rewards: -158.8318631284043, Epsilon: 0.96, Learning_rate: 0.0003\n",
      "Episode: 58, Total reward: -90.08744888841049, Average Rewards: -157.6667035650146, Epsilon: 0.96, Learning_rate: 0.0003\n",
      "Episode: 59, Total reward: -108.77940687626088, Average Rewards: -156.85191528686872, Epsilon: 0.96, Learning_rate: 0.0003\n",
      "Episode: 60, Total reward: -295.4819947710483, Average Rewards: -159.1245395407077, Epsilon: 0.96, Learning_rate: 0.0003\n",
      "Episode: 61, Total reward: -88.58244546081453, Average Rewards: -157.98676382974168, Epsilon: 0.96, Learning_rate: 0.0003\n",
      "Episode: 62, Total reward: -280.7486736997202, Average Rewards: -159.9353655737096, Epsilon: 0.96, Learning_rate: 0.0003\n",
      "Episode: 63, Total reward: -243.48698618330326, Average Rewards: -161.24085964573453, Epsilon: 0.96, Learning_rate: 0.0003\n",
      "Episode: 64, Total reward: -79.61358614967253, Average Rewards: -159.98505543810282, Epsilon: 0.96, Learning_rate: 0.0003\n",
      "Episode: 65, Total reward: -156.91987342271125, Average Rewards: -159.93861328635447, Epsilon: 0.96, Learning_rate: 0.0003\n",
      "Episode: 66, Total reward: -211.00778517743143, Average Rewards: -160.70083973248995, Epsilon: 0.96, Learning_rate: 0.0003\n",
      "Episode: 67, Total reward: -106.75402033466679, Average Rewards: -159.9075041531102, Epsilon: 0.96, Learning_rate: 0.0003\n",
      "Episode: 68, Total reward: -106.08005327462803, Average Rewards: -159.1273961693641, Epsilon: 0.96, Learning_rate: 0.0003\n",
      "Episode: 69, Total reward: -250.86418128790135, Average Rewards: -160.4379216710575, Epsilon: 0.96, Learning_rate: 0.0003\n",
      "Episode: 70, Total reward: -365.9958134390239, Average Rewards: -163.3331032452542, Epsilon: 0.96, Learning_rate: 0.0003\n",
      "Episode: 71, Total reward: -97.85311716086883, Average Rewards: -162.42365899408213, Epsilon: 0.96, Learning_rate: 0.0003\n",
      "Episode: 72, Total reward: -208.05726599359437, Average Rewards: -163.04877689818505, Epsilon: 0.95, Learning_rate: 0.0003\n",
      "Episode: 73, Total reward: -51.059388173847296, Average Rewards: -161.5354067802886, Epsilon: 0.95, Learning_rate: 0.0003\n",
      "Episode: 74, Total reward: -106.66407772112913, Average Rewards: -160.80378905949982, Epsilon: 0.95, Learning_rate: 0.0003\n",
      "Episode: 75, Total reward: -85.38388202151047, Average Rewards: -159.81142186163152, Epsilon: 0.95, Learning_rate: 0.0003\n",
      "Episode: 76, Total reward: -90.27661944152678, Average Rewards: -158.90837247955224, Epsilon: 0.95, Learning_rate: 0.0003\n",
      "Episode: 77, Total reward: -110.124776732839, Average Rewards: -158.2829417648508, Epsilon: 0.95, Learning_rate: 0.0003\n",
      "Episode: 78, Total reward: -75.80770284608109, Average Rewards: -157.23895139879042, Epsilon: 0.95, Learning_rate: 0.0003\n",
      "Episode: 79, Total reward: -211.91976871725703, Average Rewards: -157.92246161527123, Epsilon: 0.95, Learning_rate: 0.0003\n",
      "Episode: 80, Total reward: -94.39419258522527, Average Rewards: -157.13816199761635, Epsilon: 0.95, Learning_rate: 0.0003\n",
      "Episode: 81, Total reward: -294.8494730809779, Average Rewards: -158.81756823034027, Epsilon: 0.95, Learning_rate: 0.0003\n",
      "Episode: 82, Total reward: -310.94925965993184, Average Rewards: -160.6504801752751, Epsilon: 0.95, Learning_rate: 0.0003\n",
      "Episode: 83, Total reward: -223.00175890060214, Average Rewards: -161.39275730295756, Epsilon: 0.95, Learning_rate: 0.0003\n",
      "Episode: 84, Total reward: -262.47477842720593, Average Rewards: -162.58195755147813, Epsilon: 0.95, Learning_rate: 0.0003\n",
      "Episode: 85, Total reward: -28.433726442326375, Average Rewards: -161.02209439904615, Epsilon: 0.95, Learning_rate: 0.0003\n",
      "Episode: 86, Total reward: -189.22452665933065, Average Rewards: -161.3462602870954, Epsilon: 0.95, Learning_rate: 0.0003\n",
      "Episode: 87, Total reward: -82.78467043565306, Average Rewards: -160.45351494787445, Epsilon: 0.95, Learning_rate: 0.0003\n",
      "Episode: 88, Total reward: -147.43297215433915, Average Rewards: -160.30721671423922, Epsilon: 0.95, Learning_rate: 0.0003\n",
      "Episode: 89, Total reward: -193.98381273746287, Average Rewards: -160.68140111449728, Epsilon: 0.95, Learning_rate: 0.0003\n",
      "Episode: 90, Total reward: -225.27110171729214, Average Rewards: -161.3911780441983, Epsilon: 0.94, Learning_rate: 0.0003\n",
      "Episode: 91, Total reward: -163.94306536862916, Average Rewards: -161.41891594989863, Epsilon: 0.94, Learning_rate: 0.0003\n",
      "Episode: 92, Total reward: -76.39223480385475, Average Rewards: -160.50465056123147, Epsilon: 0.94, Learning_rate: 0.0003\n",
      "Episode: 93, Total reward: -155.60275214673584, Average Rewards: -160.45250270575812, Epsilon: 0.94, Learning_rate: 0.0003\n",
      "Episode: 94, Total reward: -194.67527510123745, Average Rewards: -160.8127424151842, Epsilon: 0.94, Learning_rate: 0.0003\n",
      "Episode: 95, Total reward: -180.96214749021618, Average Rewards: -161.0226320513825, Epsilon: 0.94, Learning_rate: 0.0003\n",
      "Episode: 96, Total reward: -89.09532979810582, Average Rewards: -160.2811134714518, Epsilon: 0.94, Learning_rate: 0.0003\n",
      "Episode: 97, Total reward: -2.354635506306437, Average Rewards: -158.66961879833806, Epsilon: 0.94, Learning_rate: 0.0003\n",
      "Episode: 98, Total reward: -51.63692637952046, Average Rewards: -157.58848049107726, Epsilon: 0.94, Learning_rate: 0.0003\n",
      "Episode: 99, Total reward: -132.9121969016261, Average Rewards: -157.34171765518278, Epsilon: 0.94, Learning_rate: 0.0003\n",
      "Episode: 100, Total reward: -90.4624624394825, Average Rewards: -157.34171765518278, Epsilon: 0.94, Learning_rate: 0.0003\n",
      "Episode: 101, Total reward: -79.48383382305049, Average Rewards: -157.0168697225393, Epsilon: 0.94, Learning_rate: 0.0003\n",
      "Episode: 102, Total reward: -335.36915532649687, Average Rewards: -156.67921386022041, Epsilon: 0.94, Learning_rate: 0.0003\n",
      "Episode: 103, Total reward: -36.977045153207584, Average Rewards: -159.27816459386335, Epsilon: 0.94, Learning_rate: 0.0003\n",
      "Episode: 104, Total reward: -41.04010532620792, Average Rewards: -157.18602963064615, Epsilon: 0.94, Learning_rate: 0.0003\n",
      "Episode: 105, Total reward: -84.9321264540573, Average Rewards: -156.0855386197265, Epsilon: 0.93, Learning_rate: 0.0003\n",
      "Episode: 106, Total reward: -152.73303448873378, Average Rewards: -154.13124706589704, Epsilon: 0.93, Learning_rate: 0.0003\n",
      "Episode: 107, Total reward: -225.170223305239, Average Rewards: -152.1870729042791, Epsilon: 0.93, Learning_rate: 0.0003\n",
      "Episode: 108, Total reward: -241.8620107691983, Average Rewards: -153.55379491456438, Epsilon: 0.93, Learning_rate: 0.0003\n",
      "Episode: 109, Total reward: -77.29437441857536, Average Rewards: -154.71273129413902, Epsilon: 0.93, Learning_rate: 0.0003\n",
      "Episode: 110, Total reward: -106.50591440619408, Average Rewards: -153.75137197987152, Epsilon: 0.93, Learning_rate: 0.0003\n",
      "Episode: 111, Total reward: -92.84161351683326, Average Rewards: -152.85260149937244, Epsilon: 0.93, Learning_rate: 0.0003\n",
      "Episode: 112, Total reward: -137.13035326982094, Average Rewards: -152.86783237085498, Epsilon: 0.93, Learning_rate: 0.0003\n",
      "Episode: 113, Total reward: -115.54675892865795, Average Rewards: -153.34440423347266, Epsilon: 0.93, Learning_rate: 0.0003\n",
      "Episode: 114, Total reward: -261.52134517849055, Average Rewards: -153.59586960265, Epsilon: 0.93, Learning_rate: 0.0003\n",
      "Episode: 115, Total reward: -335.10758817053556, Average Rewards: -153.56686622666788, Epsilon: 0.93, Learning_rate: 0.0003\n",
      "Episode: 116, Total reward: -98.03155067667689, Average Rewards: -153.4330292467757, Epsilon: 0.93, Learning_rate: 0.0003\n",
      "Episode: 117, Total reward: -127.58933398694926, Average Rewards: -153.1888878409891, Epsilon: 0.93, Learning_rate: 0.0003\n",
      "Episode: 118, Total reward: -98.43044033074403, Average Rewards: -153.58078603097658, Epsilon: 0.93, Learning_rate: 0.0003\n",
      "Episode: 119, Total reward: -14.89850755036072, Average Rewards: -152.41943212771127, Epsilon: 0.93, Learning_rate: 0.0003\n",
      "Episode: 120, Total reward: -203.90037117166463, Average Rewards: -151.30979103974138, Epsilon: 0.93, Learning_rate: 0.0003\n",
      "Episode: 121, Total reward: -109.12581469726543, Average Rewards: -152.1809419420263, Epsilon: 0.92, Learning_rate: 0.0003\n",
      "Episode: 122, Total reward: -136.92811123232957, Average Rewards: -151.53264992941882, Epsilon: 0.92, Learning_rate: 0.0003\n",
      "Episode: 123, Total reward: -109.4581491655097, Average Rewards: -152.1212628133039, Epsilon: 0.92, Learning_rate: 0.0003\n",
      "Episode: 124, Total reward: -95.76867776061368, Average Rewards: -152.0249474575491, Epsilon: 0.92, Learning_rate: 0.0003\n",
      "Episode: 125, Total reward: -22.75299128996339, Average Rewards: -151.89945166925858, Epsilon: 0.92, Learning_rate: 0.0003\n",
      "Episode: 126, Total reward: -382.4121401554279, Average Rewards: -150.2185177959404, Epsilon: 0.92, Learning_rate: 0.0003\n",
      "Episode: 127, Total reward: -137.83270228441376, Average Rewards: -152.96428710598764, Epsilon: 0.92, Learning_rate: 0.0003\n",
      "Episode: 128, Total reward: -81.49328520149471, Average Rewards: -153.47893757062667, Epsilon: 0.92, Learning_rate: 0.0003\n",
      "Episode: 129, Total reward: -314.73606944333187, Average Rewards: -153.1203629129617, Epsilon: 0.92, Learning_rate: 0.0003\n",
      "Episode: 130, Total reward: -290.1972670271684, Average Rewards: -154.699680933605, Epsilon: 0.92, Learning_rate: 0.0003\n",
      "Episode: 131, Total reward: -65.05989691790118, Average Rewards: -156.4937193637442, Epsilon: 0.92, Learning_rate: 0.0003\n",
      "Episode: 132, Total reward: -134.11989408804698, Average Rewards: -154.63087720001693, Epsilon: 0.92, Learning_rate: 0.0003\n",
      "Episode: 133, Total reward: -82.82901096350224, Average Rewards: -155.90336953195788, Epsilon: 0.92, Learning_rate: 0.0003\n",
      "Episode: 134, Total reward: -237.55606863258052, Average Rewards: -154.7299800695802, Epsilon: 0.92, Learning_rate: 0.0003\n",
      "Episode: 135, Total reward: -76.04666999546714, Average Rewards: -156.48055450123687, Epsilon: 0.92, Learning_rate: 0.0003\n",
      "Episode: 136, Total reward: -98.12148546582497, Average Rewards: -154.8840842440721, Epsilon: 0.92, Learning_rate: 0.0003\n",
      "Episode: 137, Total reward: -206.37778291670446, Average Rewards: -154.82939047153906, Epsilon: 0.92, Learning_rate: 0.0003\n",
      "Episode: 138, Total reward: -294.56556860880136, Average Rewards: -154.9008005480383, Epsilon: 0.92, Learning_rate: 0.0003\n",
      "Episode: 139, Total reward: -51.055149533395806, Average Rewards: -155.06178001795533, Epsilon: 0.91, Learning_rate: 0.0003\n",
      "Episode: 140, Total reward: -59.415912596303166, Average Rewards: -152.37488093548038, Epsilon: 0.91, Learning_rate: 0.0003\n",
      "Episode: 141, Total reward: -72.44432335536598, Average Rewards: -151.73715087932445, Epsilon: 0.91, Learning_rate: 0.0003\n",
      "Episode: 142, Total reward: -248.75260164104284, Average Rewards: -151.78039972389968, Epsilon: 0.91, Learning_rate: 0.0003\n",
      "Episode: 143, Total reward: -188.17540006669168, Average Rewards: -151.78060707363812, Epsilon: 0.91, Learning_rate: 0.0003\n",
      "Episode: 144, Total reward: -258.86991218232134, Average Rewards: -152.7485443604423, Epsilon: 0.91, Learning_rate: 0.0003\n",
      "Episode: 145, Total reward: -189.43278016049217, Average Rewards: -154.22010014891467, Epsilon: 0.91, Learning_rate: 0.0003\n",
      "Episode: 146, Total reward: -86.20115164765802, Average Rewards: -153.64948980987663, Epsilon: 0.91, Learning_rate: 0.0003\n",
      "Episode: 147, Total reward: -120.97226325356371, Average Rewards: -151.42621989032622, Epsilon: 0.91, Learning_rate: 0.0003\n",
      "Episode: 148, Total reward: -104.39258321867354, Average Rewards: -150.88671977236797, Epsilon: 0.91, Learning_rate: 0.0003\n",
      "Episode: 149, Total reward: -128.09822009759682, Average Rewards: -150.91004070217087, Epsilon: 0.91, Learning_rate: 0.0003\n",
      "Episode: 150, Total reward: -126.19466791101885, Average Rewards: -150.67094462492852, Epsilon: 0.91, Learning_rate: 0.0003\n",
      "Episode: 151, Total reward: -99.75817953000966, Average Rewards: -150.85863848996487, Epsilon: 0.91, Learning_rate: 0.0003\n",
      "Episode: 152, Total reward: -103.23130027557949, Average Rewards: -151.02211836961018, Epsilon: 0.91, Learning_rate: 0.0003\n",
      "Episode: 153, Total reward: -108.45981062455007, Average Rewards: -151.2584579185169, Epsilon: 0.91, Learning_rate: 0.0003\n",
      "Episode: 154, Total reward: -174.58188727220858, Average Rewards: -151.24601385985355, Epsilon: 0.91, Learning_rate: 0.0003\n",
      "Episode: 155, Total reward: -216.8724668622576, Average Rewards: -149.5734306837059, Epsilon: 0.91, Learning_rate: 0.0003\n",
      "Episode: 156, Total reward: -223.3714023609883, Average Rewards: -149.82266920530196, Epsilon: 0.90, Learning_rate: 0.0003\n",
      "Episode: 157, Total reward: -152.98878832016845, Average Rewards: -150.7998788738977, Epsilon: 0.90, Learning_rate: 0.0003\n",
      "Episode: 158, Total reward: -120.96760478173806, Average Rewards: -150.67402239518233, Epsilon: 0.90, Learning_rate: 0.0003\n",
      "Episode: 159, Total reward: -94.83604197828603, Average Rewards: -150.98282395411562, Epsilon: 0.90, Learning_rate: 0.0003\n",
      "Episode: 160, Total reward: -91.84968962417865, Average Rewards: -150.84339030513587, Epsilon: 0.90, Learning_rate: 0.0003\n",
      "Episode: 161, Total reward: -77.80127704454813, Average Rewards: -148.80706725366719, Epsilon: 0.90, Learning_rate: 0.0003\n",
      "Episode: 162, Total reward: -109.48568422996142, Average Rewards: -148.69925556950452, Epsilon: 0.90, Learning_rate: 0.0003\n",
      "Episode: 163, Total reward: -75.17822447709358, Average Rewards: -146.98662567480696, Epsilon: 0.90, Learning_rate: 0.0003\n",
      "Episode: 164, Total reward: -164.0546918577444, Average Rewards: -145.30353805774482, Epsilon: 0.90, Learning_rate: 0.0003\n",
      "Episode: 165, Total reward: -127.88538671124294, Average Rewards: -146.14794911482556, Epsilon: 0.90, Learning_rate: 0.0003\n",
      "Episode: 166, Total reward: -189.32711098991012, Average Rewards: -145.85760424771087, Epsilon: 0.90, Learning_rate: 0.0003\n",
      "Episode: 167, Total reward: 26.830904624866875, Average Rewards: -145.64079750583568, Epsilon: 0.90, Learning_rate: 0.0003\n",
      "Episode: 168, Total reward: -197.5445633854755, Average Rewards: -144.30494825624032, Epsilon: 0.90, Learning_rate: 0.0003\n",
      "Episode: 169, Total reward: -42.650508104009496, Average Rewards: -145.21959335734883, Epsilon: 0.90, Learning_rate: 0.0003\n",
      "Episode: 170, Total reward: -51.250054943426846, Average Rewards: -143.13745662550988, Epsilon: 0.90, Learning_rate: 0.0003\n",
      "Episode: 171, Total reward: -55.97757661182052, Average Rewards: -139.9899990405539, Epsilon: 0.90, Learning_rate: 0.0003\n",
      "Episode: 172, Total reward: -265.5729148670977, Average Rewards: -139.5712436350634, Epsilon: 0.89, Learning_rate: 0.0003\n",
      "Episode: 173, Total reward: -155.69496196578612, Average Rewards: -140.14640012379846, Epsilon: 0.89, Learning_rate: 0.0003\n",
      "Episode: 174, Total reward: -55.90812903579811, Average Rewards: -141.19275586171784, Epsilon: 0.89, Learning_rate: 0.0003\n",
      "Episode: 175, Total reward: -117.76991165920977, Average Rewards: -140.68519637486457, Epsilon: 0.89, Learning_rate: 0.0003\n",
      "Episode: 176, Total reward: -75.61579213625473, Average Rewards: -141.00905667124155, Epsilon: 0.89, Learning_rate: 0.0003\n",
      "Episode: 177, Total reward: -125.65600856244032, Average Rewards: -140.86244839818883, Epsilon: 0.89, Learning_rate: 0.0003\n",
      "Episode: 178, Total reward: -95.81383149245977, Average Rewards: -141.01776071648487, Epsilon: 0.89, Learning_rate: 0.0003\n",
      "Episode: 179, Total reward: -90.00439297957747, Average Rewards: -141.21782200294865, Epsilon: 0.89, Learning_rate: 0.0003\n",
      "Episode: 180, Total reward: -73.85469873582088, Average Rewards: -139.99866824557185, Epsilon: 0.89, Learning_rate: 0.0003\n",
      "Episode: 181, Total reward: -262.5587811484097, Average Rewards: -139.79327330707775, Epsilon: 0.89, Learning_rate: 0.0003\n",
      "Episode: 182, Total reward: -374.5533359798791, Average Rewards: -139.47036638775208, Epsilon: 0.89, Learning_rate: 0.0003\n",
      "Episode: 183, Total reward: -94.55044355339543, Average Rewards: -140.10640715095155, Epsilon: 0.89, Learning_rate: 0.0003\n",
      "Episode: 184, Total reward: -10.96305822590432, Average Rewards: -138.82189399747946, Epsilon: 0.89, Learning_rate: 0.0003\n",
      "Episode: 185, Total reward: -105.9376628857694, Average Rewards: -136.30677679546648, Epsilon: 0.89, Learning_rate: 0.0003\n",
      "Episode: 186, Total reward: -72.90070235326755, Average Rewards: -137.08181615990094, Epsilon: 0.89, Learning_rate: 0.0003\n",
      "Episode: 187, Total reward: -141.71092379736345, Average Rewards: -135.9185779168403, Epsilon: 0.88, Learning_rate: 0.0003\n",
      "Episode: 188, Total reward: -107.1447197928872, Average Rewards: -136.5078404504574, Epsilon: 0.88, Learning_rate: 0.0003\n",
      "Episode: 189, Total reward: -100.31600006522353, Average Rewards: -136.10495792684287, Epsilon: 0.88, Learning_rate: 0.0003\n",
      "Episode: 190, Total reward: -199.28546362968643, Average Rewards: -135.1682798001205, Epsilon: 0.88, Learning_rate: 0.0003\n",
      "Episode: 191, Total reward: -123.92804842356685, Average Rewards: -134.90842341924443, Epsilon: 0.88, Learning_rate: 0.0003\n",
      "Episode: 192, Total reward: 49.04244868914435, Average Rewards: -134.5082732497938, Epsilon: 0.88, Learning_rate: 0.0003\n",
      "Episode: 193, Total reward: -159.06166492946693, Average Rewards: -133.25392641486383, Epsilon: 0.88, Learning_rate: 0.0003\n",
      "Episode: 194, Total reward: -94.8790337568576, Average Rewards: -133.2885155426911, Epsilon: 0.88, Learning_rate: 0.0003\n",
      "Episode: 195, Total reward: -295.8549176411278, Average Rewards: -132.2905531292473, Epsilon: 0.88, Learning_rate: 0.0003\n",
      "Episode: 196, Total reward: -120.88063070592301, Average Rewards: -133.43948083075642, Epsilon: 0.88, Learning_rate: 0.0003\n",
      "Episode: 197, Total reward: -122.39366988179856, Average Rewards: -133.7573338398346, Epsilon: 0.88, Learning_rate: 0.0003\n",
      "Episode: 198, Total reward: -243.54655682106227, Average Rewards: -134.9577241835895, Epsilon: 0.88, Learning_rate: 0.0003\n",
      "Episode: 199, Total reward: -233.13590831351664, Average Rewards: -136.8768204880049, Epsilon: 0.88, Learning_rate: 0.0003\n",
      "Episode: 200, Total reward: -126.20375415427704, Average Rewards: -137.87905760212382, Epsilon: 0.87, Learning_rate: 0.0003\n",
      "Episode: 201, Total reward: -84.06932783404056, Average Rewards: -138.23647051927182, Epsilon: 0.87, Learning_rate: 0.0003\n",
      "Episode: 202, Total reward: -197.1301536852247, Average Rewards: -138.2823254593817, Epsilon: 0.87, Learning_rate: 0.0003\n",
      "Episode: 203, Total reward: -158.65438288178893, Average Rewards: -136.89993544296897, Epsilon: 0.87, Learning_rate: 0.0003\n",
      "Episode: 204, Total reward: -72.3457931226977, Average Rewards: -138.11670882025481, Epsilon: 0.87, Learning_rate: 0.0003\n",
      "Episode: 205, Total reward: -104.23126828974037, Average Rewards: -138.42976569821968, Epsilon: 0.87, Learning_rate: 0.0003\n",
      "Episode: 206, Total reward: -79.68713115383241, Average Rewards: -138.62275711657654, Epsilon: 0.87, Learning_rate: 0.0003\n",
      "Episode: 207, Total reward: -299.50613887888255, Average Rewards: -137.89229808322753, Epsilon: 0.87, Learning_rate: 0.0003\n",
      "Episode: 208, Total reward: -261.8090796338124, Average Rewards: -138.63565723896397, Epsilon: 0.87, Learning_rate: 0.0003\n",
      "Episode: 209, Total reward: -82.62588635124933, Average Rewards: -138.83512792761007, Epsilon: 0.87, Learning_rate: 0.0003\n",
      "Episode: 210, Total reward: -76.89343263837408, Average Rewards: -138.8884430469368, Epsilon: 0.87, Learning_rate: 0.0003\n",
      "Episode: 211, Total reward: -109.67132908858301, Average Rewards: -138.5923182292586, Epsilon: 0.87, Learning_rate: 0.0003\n",
      "Episode: 212, Total reward: -41.040739591078435, Average Rewards: -138.76061538497612, Epsilon: 0.87, Learning_rate: 0.0003\n",
      "Episode: 213, Total reward: -119.29721390286932, Average Rewards: -137.7997192481887, Epsilon: 0.87, Learning_rate: 0.0003\n",
      "Episode: 214, Total reward: -75.2346101289311, Average Rewards: -137.83722379793082, Epsilon: 0.87, Learning_rate: 0.0003\n",
      "Episode: 215, Total reward: -102.54972565681547, Average Rewards: -135.97435644743524, Epsilon: 0.87, Learning_rate: 0.0003\n",
      "Episode: 216, Total reward: -96.46768948496147, Average Rewards: -133.64877782229803, Epsilon: 0.86, Learning_rate: 0.0003\n",
      "Episode: 217, Total reward: -79.43081688514172, Average Rewards: -133.63313921038088, Epsilon: 0.86, Learning_rate: 0.0003\n",
      "Episode: 218, Total reward: -96.33651698120208, Average Rewards: -133.1515540393628, Epsilon: 0.86, Learning_rate: 0.0003\n",
      "Episode: 219, Total reward: -77.53863642197234, Average Rewards: -133.13061480586737, Epsilon: 0.86, Learning_rate: 0.0003\n",
      "Episode: 220, Total reward: -152.4772519321443, Average Rewards: -133.7570160945835, Epsilon: 0.86, Learning_rate: 0.0003\n",
      "Episode: 221, Total reward: -148.0464996751868, Average Rewards: -133.2427849021883, Epsilon: 0.86, Learning_rate: 0.0003\n",
      "Episode: 222, Total reward: -82.71101373074636, Average Rewards: -133.6319917519675, Epsilon: 0.86, Learning_rate: 0.0003\n",
      "Episode: 223, Total reward: -104.07072081103786, Average Rewards: -133.08982077695168, Epsilon: 0.86, Learning_rate: 0.0003\n",
      "Episode: 224, Total reward: -96.56200244105953, Average Rewards: -133.03594649340695, Epsilon: 0.86, Learning_rate: 0.0003\n",
      "Episode: 225, Total reward: -115.36952915526543, Average Rewards: -133.04387974021142, Epsilon: 0.86, Learning_rate: 0.0003\n",
      "Episode: 226, Total reward: -130.44237351094364, Average Rewards: -133.97004511886442, Epsilon: 0.86, Learning_rate: 0.0003\n",
      "Episode: 227, Total reward: -161.54982345419108, Average Rewards: -131.45034745241958, Epsilon: 0.86, Learning_rate: 0.0003\n",
      "Episode: 228, Total reward: 10.691933150493412, Average Rewards: -131.68751866411736, Epsilon: 0.86, Learning_rate: 0.0003\n",
      "Episode: 229, Total reward: -77.4241310257001, Average Rewards: -130.76566648059745, Epsilon: 0.86, Learning_rate: 0.0003\n",
      "Episode: 230, Total reward: -342.6090745569346, Average Rewards: -128.39254709642114, Epsilon: 0.85, Learning_rate: 0.0003\n",
      "Episode: 231, Total reward: -189.145180975586, Average Rewards: -128.91666517171882, Epsilon: 0.85, Learning_rate: 0.0003\n",
      "Episode: 232, Total reward: -72.13319991886524, Average Rewards: -130.15751801229564, Epsilon: 0.85, Learning_rate: 0.0003\n",
      "Episode: 233, Total reward: -124.17532715344011, Average Rewards: -129.53765107060383, Epsilon: 0.85, Learning_rate: 0.0003\n",
      "Episode: 234, Total reward: -85.50101581801366, Average Rewards: -129.95111423250322, Epsilon: 0.85, Learning_rate: 0.0003\n",
      "Episode: 235, Total reward: -112.17347586641453, Average Rewards: -128.43056370435755, Epsilon: 0.85, Learning_rate: 0.0003\n",
      "Episode: 236, Total reward: -123.73250196177034, Average Rewards: -128.79183176306705, Epsilon: 0.85, Learning_rate: 0.0003\n",
      "Episode: 237, Total reward: -114.38888544293509, Average Rewards: -129.0479419280265, Epsilon: 0.85, Learning_rate: 0.0003\n",
      "Episode: 238, Total reward: -128.76862995274846, Average Rewards: -128.1280529532888, Epsilon: 0.85, Learning_rate: 0.0003\n",
      "Episode: 239, Total reward: -67.19245161427749, Average Rewards: -126.47008356672826, Epsilon: 0.85, Learning_rate: 0.0003\n",
      "Episode: 240, Total reward: -161.64842791942164, Average Rewards: -126.63145658753709, Epsilon: 0.85, Learning_rate: 0.0003\n",
      "Episode: 241, Total reward: -94.82692889677499, Average Rewards: -127.65378174076824, Epsilon: 0.85, Learning_rate: 0.0003\n",
      "Episode: 242, Total reward: -101.67932255979547, Average Rewards: -127.87760779618235, Epsilon: 0.85, Learning_rate: 0.0003\n",
      "Episode: 243, Total reward: -131.32775099229497, Average Rewards: -126.40687500536986, Epsilon: 0.85, Learning_rate: 0.0003\n",
      "Episode: 244, Total reward: -70.2240481989599, Average Rewards: -125.83839851462591, Epsilon: 0.85, Learning_rate: 0.0003\n",
      "Episode: 245, Total reward: -80.2007278424951, Average Rewards: -123.9519398747923, Epsilon: 0.84, Learning_rate: 0.0003\n",
      "Episode: 246, Total reward: -177.13160439693797, Average Rewards: -122.85961935161232, Epsilon: 0.84, Learning_rate: 0.0003\n",
      "Episode: 247, Total reward: -64.84947478639405, Average Rewards: -123.76892387910513, Epsilon: 0.84, Learning_rate: 0.0003\n",
      "Episode: 248, Total reward: -99.65045740280192, Average Rewards: -123.20769599443344, Epsilon: 0.84, Learning_rate: 0.0003\n",
      "Episode: 249, Total reward: -70.95531323976181, Average Rewards: -123.16027473627472, Epsilon: 0.84, Learning_rate: 0.0003\n",
      "Episode: 250, Total reward: -72.4876124674599, Average Rewards: -122.58884566769635, Epsilon: 0.84, Learning_rate: 0.0003\n",
      "Episode: 251, Total reward: -101.94602629127617, Average Rewards: -122.05177511326076, Epsilon: 0.84, Learning_rate: 0.0003\n",
      "Episode: 252, Total reward: -60.83534094787066, Average Rewards: -122.07365358087344, Epsilon: 0.84, Learning_rate: 0.0003\n",
      "Episode: 253, Total reward: -143.97218540604752, Average Rewards: -121.64969398759632, Epsilon: 0.84, Learning_rate: 0.0003\n",
      "Episode: 254, Total reward: -22.155161619692862, Average Rewards: -122.00481773541132, Epsilon: 0.84, Learning_rate: 0.0003\n",
      "Episode: 255, Total reward: -389.3213181689339, Average Rewards: -120.48055047888616, Epsilon: 0.84, Learning_rate: 0.0003\n",
      "Episode: 256, Total reward: -35.44409204837518, Average Rewards: -122.20503899195293, Epsilon: 0.84, Learning_rate: 0.0003\n",
      "Episode: 257, Total reward: -132.63454907999247, Average Rewards: -120.32576588882681, Epsilon: 0.84, Learning_rate: 0.0003\n",
      "Episode: 258, Total reward: -6.9315514469903405, Average Rewards: -120.12222349642504, Epsilon: 0.84, Learning_rate: 0.0003\n",
      "Episode: 259, Total reward: -185.73990548252834, Average Rewards: -118.98186296307756, Epsilon: 0.83, Learning_rate: 0.0003\n",
      "Episode: 260, Total reward: -29.750506421172773, Average Rewards: -119.89090159812, Epsilon: 0.83, Learning_rate: 0.0003\n",
      "Episode: 261, Total reward: -119.80218446614958, Average Rewards: -119.26990976608995, Epsilon: 0.83, Learning_rate: 0.0003\n",
      "Episode: 262, Total reward: -99.48876123972286, Average Rewards: -119.68991884030595, Epsilon: 0.83, Learning_rate: 0.0003\n",
      "Episode: 263, Total reward: -84.1683925839725, Average Rewards: -119.58994961040356, Epsilon: 0.83, Learning_rate: 0.0003\n",
      "Episode: 264, Total reward: -141.42880860550616, Average Rewards: -119.67985129147235, Epsilon: 0.83, Learning_rate: 0.0003\n",
      "Episode: 265, Total reward: -122.03888210881472, Average Rewards: -119.45359245894997, Epsilon: 0.83, Learning_rate: 0.0003\n",
      "Episode: 266, Total reward: -142.92282875644582, Average Rewards: -119.39512741292569, Epsilon: 0.83, Learning_rate: 0.0003\n",
      "Episode: 267, Total reward: -52.93046464099615, Average Rewards: -118.93108459059104, Epsilon: 0.83, Learning_rate: 0.0003\n",
      "Episode: 268, Total reward: -82.66984929393001, Average Rewards: -119.72869828324967, Epsilon: 0.83, Learning_rate: 0.0003\n",
      "Episode: 269, Total reward: -49.2593745100485, Average Rewards: -118.57995114233421, Epsilon: 0.83, Learning_rate: 0.0003\n",
      "Episode: 270, Total reward: -79.8136160452455, Average Rewards: -118.64603980639463, Epsilon: 0.83, Learning_rate: 0.0003\n",
      "Episode: 271, Total reward: -139.30127410131854, Average Rewards: -118.9316754174128, Epsilon: 0.83, Learning_rate: 0.0003\n",
      "Episode: 272, Total reward: -282.9982558693729, Average Rewards: -119.76491239230778, Epsilon: 0.83, Learning_rate: 0.0003\n",
      "Episode: 273, Total reward: -160.65287328801836, Average Rewards: -119.93916580233054, Epsilon: 0.83, Learning_rate: 0.0003\n",
      "Episode: 274, Total reward: -100.91289965988341, Average Rewards: -119.98874491555286, Epsilon: 0.82, Learning_rate: 0.0003\n",
      "Episode: 275, Total reward: -53.01065367125022, Average Rewards: -120.4387926217937, Epsilon: 0.82, Learning_rate: 0.0003\n",
      "Episode: 276, Total reward: -104.49853732843147, Average Rewards: -119.7912000419141, Epsilon: 0.82, Learning_rate: 0.0003\n",
      "Episode: 277, Total reward: -80.03879522614756, Average Rewards: -120.08002749383584, Epsilon: 0.82, Learning_rate: 0.0003\n",
      "Episode: 278, Total reward: -98.17964343677077, Average Rewards: -119.62385536047293, Epsilon: 0.82, Learning_rate: 0.0003\n",
      "Episode: 279, Total reward: -87.14481128180952, Average Rewards: -119.64751347991606, Epsilon: 0.82, Learning_rate: 0.0003\n",
      "Episode: 280, Total reward: -77.90846380484058, Average Rewards: -119.61891766293837, Epsilon: 0.82, Learning_rate: 0.0003\n",
      "Episode: 281, Total reward: -156.36248865807823, Average Rewards: -119.65945531362857, Epsilon: 0.82, Learning_rate: 0.0003\n",
      "Episode: 282, Total reward: -142.93910989633775, Average Rewards: -118.59749238872526, Epsilon: 0.82, Learning_rate: 0.0003\n",
      "Episode: 283, Total reward: -61.72013642415529, Average Rewards: -116.28135012788982, Epsilon: 0.82, Learning_rate: 0.0003\n",
      "Episode: 284, Total reward: -126.11864881788385, Average Rewards: -115.95304705659743, Epsilon: 0.82, Learning_rate: 0.0003\n",
      "Episode: 285, Total reward: -109.10021110865024, Average Rewards: -117.10460296251723, Epsilon: 0.82, Learning_rate: 0.0003\n",
      "Episode: 286, Total reward: -41.1556645094733, Average Rewards: -117.13622844474605, Epsilon: 0.82, Learning_rate: 0.0003\n",
      "Episode: 287, Total reward: -5.742104372178062, Average Rewards: -116.81877806630811, Epsilon: 0.82, Learning_rate: 0.0003\n",
      "Episode: 288, Total reward: -89.90349027997678, Average Rewards: -115.45908987205625, Epsilon: 0.82, Learning_rate: 0.0003\n",
      "Episode: 289, Total reward: -107.70948514644411, Average Rewards: -115.28667757692718, Epsilon: 0.81, Learning_rate: 0.0003\n",
      "Episode: 290, Total reward: -97.83268110797856, Average Rewards: -115.36061242773935, Epsilon: 0.81, Learning_rate: 0.0003\n",
      "Episode: 291, Total reward: 30.620252197449048, Average Rewards: -114.3460846025223, Epsilon: 0.81, Learning_rate: 0.0003\n",
      "Episode: 292, Total reward: -134.89376178012935, Average Rewards: -112.80060159631212, Epsilon: 0.81, Learning_rate: 0.0003\n",
      "Episode: 293, Total reward: -175.0246429712085, Average Rewards: -114.63996370100486, Epsilon: 0.81, Learning_rate: 0.0003\n",
      "Episode: 294, Total reward: -173.656579378827, Average Rewards: -114.79959348142228, Epsilon: 0.81, Learning_rate: 0.0003\n",
      "Episode: 295, Total reward: 16.960554111092023, Average Rewards: -115.58736893764195, Epsilon: 0.81, Learning_rate: 0.0003\n",
      "Episode: 296, Total reward: -92.01664977022796, Average Rewards: -112.45921422011973, Epsilon: 0.81, Learning_rate: 0.0003\n",
      "Episode: 297, Total reward: -259.3803869676417, Average Rewards: -112.17057441076278, Epsilon: 0.81, Learning_rate: 0.0003\n",
      "Episode: 298, Total reward: -183.42498221623833, Average Rewards: -113.54044158162122, Epsilon: 0.81, Learning_rate: 0.0003\n",
      "Episode: 299, Total reward: -105.01577929345177, Average Rewards: -112.93922583557298, Epsilon: 0.81, Learning_rate: 0.0003\n",
      "Episode: 300, Total reward: -75.40933066589457, Average Rewards: -111.65802454537234, Epsilon: 0.81, Learning_rate: 0.0003\n",
      "Episode: 301, Total reward: -139.84613418365768, Average Rewards: -111.1500803104885, Epsilon: 0.81, Learning_rate: 0.0003\n",
      "Episode: 302, Total reward: -95.67940774414312, Average Rewards: -111.70784837398469, Epsilon: 0.81, Learning_rate: 0.0003\n",
      "Episode: 303, Total reward: -58.135488788431374, Average Rewards: -110.69334091457385, Epsilon: 0.81, Learning_rate: 0.0003\n",
      "Episode: 304, Total reward: -58.425726735475074, Average Rewards: -109.68815197364029, Epsilon: 0.80, Learning_rate: 0.0003\n",
      "Episode: 305, Total reward: -98.57262301270671, Average Rewards: -109.54895130976807, Epsilon: 0.80, Learning_rate: 0.0003\n",
      "Episode: 306, Total reward: -23.076338155507557, Average Rewards: -109.49236485699775, Epsilon: 0.80, Learning_rate: 0.0003\n",
      "Episode: 307, Total reward: -123.8870886578225, Average Rewards: -108.9262569270145, Epsilon: 0.80, Learning_rate: 0.0003\n",
      "Episode: 308, Total reward: -177.95952279479732, Average Rewards: -107.1700664248039, Epsilon: 0.80, Learning_rate: 0.0003\n",
      "Episode: 309, Total reward: -249.34272501442692, Average Rewards: -106.33157085641376, Epsilon: 0.80, Learning_rate: 0.0003\n",
      "Episode: 310, Total reward: -33.37094006384909, Average Rewards: -107.99873924304553, Epsilon: 0.80, Learning_rate: 0.0003\n",
      "Episode: 311, Total reward: -182.71465265235514, Average Rewards: -107.56351431730027, Epsilon: 0.80, Learning_rate: 0.0003\n",
      "Episode: 312, Total reward: -104.09094050292289, Average Rewards: -108.293947552938, Epsilon: 0.80, Learning_rate: 0.0003\n",
      "Episode: 313, Total reward: -101.52480176016435, Average Rewards: -108.92444956205644, Epsilon: 0.80, Learning_rate: 0.0003\n",
      "Episode: 314, Total reward: -116.29338423426435, Average Rewards: -108.7467254406294, Epsilon: 0.80, Learning_rate: 0.0003\n",
      "Episode: 315, Total reward: -292.62595978560887, Average Rewards: -109.15731318168272, Epsilon: 0.80, Learning_rate: 0.0003\n",
      "Episode: 316, Total reward: -51.9812716748022, Average Rewards: -111.05807552297067, Epsilon: 0.80, Learning_rate: 0.0003\n",
      "Episode: 317, Total reward: -19.722529399924184, Average Rewards: -110.61321134486906, Epsilon: 0.80, Learning_rate: 0.0003\n",
      "Episode: 318, Total reward: -91.74207807888959, Average Rewards: -110.01612847001688, Epsilon: 0.79, Learning_rate: 0.0003\n",
      "Episode: 319, Total reward: -119.02194510058229, Average Rewards: -109.97018408099375, Epsilon: 0.79, Learning_rate: 0.0003\n",
      "Episode: 320, Total reward: -70.49659343869388, Average Rewards: -110.38501716777984, Epsilon: 0.79, Learning_rate: 0.0003\n",
      "Episode: 321, Total reward: -116.2453624533083, Average Rewards: -109.56521058284534, Epsilon: 0.79, Learning_rate: 0.0003\n",
      "Episode: 322, Total reward: -82.07779288038081, Average Rewards: -109.24719921062656, Epsilon: 0.79, Learning_rate: 0.0003\n",
      "Episode: 323, Total reward: -37.183396866786325, Average Rewards: -109.24086700212291, Epsilon: 0.79, Learning_rate: 0.0003\n",
      "Episode: 324, Total reward: -77.58349962533163, Average Rewards: -108.57199376268039, Epsilon: 0.79, Learning_rate: 0.0003\n",
      "Episode: 325, Total reward: -105.97652070297228, Average Rewards: -108.38220873452313, Epsilon: 0.79, Learning_rate: 0.0003\n",
      "Episode: 326, Total reward: -27.011919932637056, Average Rewards: -108.2882786500002, Epsilon: 0.79, Learning_rate: 0.0003\n",
      "Episode: 327, Total reward: -101.8103312401299, Average Rewards: -107.25397411421714, Epsilon: 0.79, Learning_rate: 0.0003\n",
      "Episode: 328, Total reward: -68.79669423973667, Average Rewards: -106.65657919207652, Epsilon: 0.79, Learning_rate: 0.0003\n",
      "Episode: 329, Total reward: -67.2833934729363, Average Rewards: -107.4514654659788, Epsilon: 0.79, Learning_rate: 0.0003\n",
      "Episode: 330, Total reward: -131.90981132838039, Average Rewards: -107.35005809045114, Epsilon: 0.79, Learning_rate: 0.0003\n",
      "Episode: 331, Total reward: -76.39891099025544, Average Rewards: -105.24306545816562, Epsilon: 0.79, Learning_rate: 0.0003\n",
      "Episode: 332, Total reward: -96.47938807202804, Average Rewards: -104.11560275831232, Epsilon: 0.78, Learning_rate: 0.0003\n",
      "Episode: 333, Total reward: -120.09845216177254, Average Rewards: -104.35906463984396, Epsilon: 0.78, Learning_rate: 0.0003\n",
      "Episode: 334, Total reward: -73.7791934553982, Average Rewards: -104.31829588992727, Epsilon: 0.78, Learning_rate: 0.0003\n",
      "Episode: 335, Total reward: -101.33228167162831, Average Rewards: -104.20107766630112, Epsilon: 0.78, Learning_rate: 0.0003\n",
      "Episode: 336, Total reward: -151.57599840893516, Average Rewards: -104.09266572435325, Epsilon: 0.78, Learning_rate: 0.0003\n",
      "Episode: 337, Total reward: -35.756081571516404, Average Rewards: -104.3711006888249, Epsilon: 0.78, Learning_rate: 0.0003\n",
      "Episode: 338, Total reward: -83.22356078001128, Average Rewards: -103.58477265011072, Epsilon: 0.78, Learning_rate: 0.0003\n",
      "Episode: 339, Total reward: -69.18563350252703, Average Rewards: -103.12932195838333, Epsilon: 0.78, Learning_rate: 0.0003\n",
      "Episode: 340, Total reward: 81.38595564239552, Average Rewards: -103.14925377726586, Epsilon: 0.77, Learning_rate: 0.0003\n",
      "Episode: 341, Total reward: -96.40501041527531, Average Rewards: -100.7189099416477, Epsilon: 0.77, Learning_rate: 0.0003\n",
      "Episode: 342, Total reward: -93.09633102914553, Average Rewards: -100.73469075683269, Epsilon: 0.77, Learning_rate: 0.0003\n",
      "Episode: 343, Total reward: -67.31493453579569, Average Rewards: -100.6488608415262, Epsilon: 0.77, Learning_rate: 0.0003\n",
      "Episode: 344, Total reward: -105.26776598955576, Average Rewards: -100.0087326769612, Epsilon: 0.77, Learning_rate: 0.0003\n",
      "Episode: 345, Total reward: -38.956664419466115, Average Rewards: -100.35916985486718, Epsilon: 0.77, Learning_rate: 0.0003\n",
      "Episode: 346, Total reward: -107.46971743960974, Average Rewards: -99.94672922063688, Epsilon: 0.77, Learning_rate: 0.0003\n",
      "Episode: 347, Total reward: -108.68257460069736, Average Rewards: -99.25011035106358, Epsilon: 0.77, Learning_rate: 0.0003\n",
      "Episode: 348, Total reward: -59.95538404450838, Average Rewards: -99.6884413492066, Epsilon: 0.77, Learning_rate: 0.0003\n",
      "Episode: 349, Total reward: -151.54756792732496, Average Rewards: -99.29149061562366, Epsilon: 0.77, Learning_rate: 0.0003\n",
      "Episode: 350, Total reward: -112.91486890004063, Average Rewards: -100.09741316249932, Epsilon: 0.77, Learning_rate: 0.0003\n",
      "Episode: 351, Total reward: -136.93812932335027, Average Rewards: -100.5016857268251, Epsilon: 0.77, Learning_rate: 0.0003\n",
      "Episode: 352, Total reward: -16.327141926596454, Average Rewards: -100.85160675714586, Epsilon: 0.77, Learning_rate: 0.0003\n",
      "Episode: 353, Total reward: -42.312378698159236, Average Rewards: -100.40652476693313, Epsilon: 0.77, Learning_rate: 0.0003\n",
      "Episode: 354, Total reward: -97.89552327866458, Average Rewards: -99.38992669985424, Epsilon: 0.76, Learning_rate: 0.0003\n",
      "Episode: 355, Total reward: -112.61213232895346, Average Rewards: -100.14733031644397, Epsilon: 0.76, Learning_rate: 0.0003\n",
      "Episode: 356, Total reward: -122.07985537224864, Average Rewards: -97.38023845804415, Epsilon: 0.76, Learning_rate: 0.0003\n",
      "Episode: 357, Total reward: -95.70740559044137, Average Rewards: -98.24659609128288, Epsilon: 0.76, Learning_rate: 0.0003\n",
      "Episode: 358, Total reward: -60.15136060411923, Average Rewards: -97.87732465638737, Epsilon: 0.76, Learning_rate: 0.0003\n",
      "Episode: 359, Total reward: -93.25074212799305, Average Rewards: -98.40952274795865, Epsilon: 0.76, Learning_rate: 0.0003\n",
      "Episode: 360, Total reward: -85.34219609486325, Average Rewards: -97.48463111441329, Epsilon: 0.76, Learning_rate: 0.0003\n",
      "Episode: 361, Total reward: -43.17887309489814, Average Rewards: -98.04054801115021, Epsilon: 0.76, Learning_rate: 0.0003\n",
      "Episode: 362, Total reward: -153.1454391047498, Average Rewards: -97.27431489743768, Epsilon: 0.76, Learning_rate: 0.0003\n",
      "Episode: 363, Total reward: -134.68224813839413, Average Rewards: -97.81088167608794, Epsilon: 0.76, Learning_rate: 0.0003\n",
      "Episode: 364, Total reward: -56.87089401619549, Average Rewards: -98.31602023163214, Epsilon: 0.76, Learning_rate: 0.0003\n",
      "Episode: 365, Total reward: -141.06119057122692, Average Rewards: -97.47044108573905, Epsilon: 0.76, Learning_rate: 0.0003\n",
      "Episode: 366, Total reward: -70.66271889401938, Average Rewards: -97.66066417036316, Epsilon: 0.76, Learning_rate: 0.0003\n",
      "Episode: 367, Total reward: -74.95410386467685, Average Rewards: -96.93806307173891, Epsilon: 0.76, Learning_rate: 0.0003\n",
      "Episode: 368, Total reward: -74.1872895281272, Average Rewards: -97.15829946397572, Epsilon: 0.75, Learning_rate: 0.0003\n",
      "Episode: 369, Total reward: -52.420243396771056, Average Rewards: -97.07347386631768, Epsilon: 0.75, Learning_rate: 0.0003\n",
      "Episode: 370, Total reward: -47.94991461460949, Average Rewards: -97.10508255518492, Epsilon: 0.75, Learning_rate: 0.0003\n",
      "Episode: 371, Total reward: -52.98329959292214, Average Rewards: -96.78644554087856, Epsilon: 0.75, Learning_rate: 0.0003\n",
      "Episode: 372, Total reward: -65.15399858083383, Average Rewards: -95.92326579579459, Epsilon: 0.75, Learning_rate: 0.0003\n",
      "Episode: 373, Total reward: -91.88550286997022, Average Rewards: -93.74482322290922, Epsilon: 0.75, Learning_rate: 0.0003\n",
      "Episode: 374, Total reward: -71.06282614990184, Average Rewards: -93.05714951872875, Epsilon: 0.75, Learning_rate: 0.0003\n",
      "Episode: 375, Total reward: -70.86297488378908, Average Rewards: -92.75864878362893, Epsilon: 0.75, Learning_rate: 0.0003\n",
      "Episode: 376, Total reward: -146.24589462651184, Average Rewards: -92.93717199575431, Epsilon: 0.75, Learning_rate: 0.0003\n",
      "Episode: 377, Total reward: -57.917013084144514, Average Rewards: -93.35464556873512, Epsilon: 0.75, Learning_rate: 0.0003\n",
      "Episode: 378, Total reward: -220.2661973762817, Average Rewards: -93.13342774731507, Epsilon: 0.75, Learning_rate: 0.0003\n",
      "Episode: 379, Total reward: -20.076249669239203, Average Rewards: -94.3542932867102, Epsilon: 0.75, Learning_rate: 0.0003\n",
      "Episode: 380, Total reward: -64.15617905374438, Average Rewards: -93.68360767058448, Epsilon: 0.75, Learning_rate: 0.0003\n",
      "Episode: 381, Total reward: -68.70024897847183, Average Rewards: -93.54608482307354, Epsilon: 0.75, Learning_rate: 0.0003\n",
      "Episode: 382, Total reward: -81.36611746734528, Average Rewards: -92.66946242627746, Epsilon: 0.74, Learning_rate: 0.0003\n",
      "Episode: 383, Total reward: -116.65726765730297, Average Rewards: -92.05373250198753, Epsilon: 0.74, Learning_rate: 0.0003\n",
      "Episode: 384, Total reward: -80.48099480064347, Average Rewards: -92.603103814319, Epsilon: 0.74, Learning_rate: 0.0003\n",
      "Episode: 385, Total reward: -203.33523937789215, Average Rewards: -92.14672727414661, Epsilon: 0.74, Learning_rate: 0.0003\n",
      "Episode: 386, Total reward: -169.2691018690354, Average Rewards: -93.08907755683902, Epsilon: 0.74, Learning_rate: 0.0003\n",
      "Episode: 387, Total reward: -61.469310686151495, Average Rewards: -94.37021193043465, Epsilon: 0.74, Learning_rate: 0.0003\n",
      "Episode: 388, Total reward: -116.58502834253088, Average Rewards: -94.92748399357438, Epsilon: 0.74, Learning_rate: 0.0003\n",
      "Episode: 389, Total reward: -85.28472028714941, Average Rewards: -95.19429937419993, Epsilon: 0.74, Learning_rate: 0.0003\n",
      "Episode: 390, Total reward: -94.78748261357592, Average Rewards: -94.97005172560698, Epsilon: 0.74, Learning_rate: 0.0003\n",
      "Episode: 391, Total reward: -32.48969329099138, Average Rewards: -94.93959974066296, Epsilon: 0.74, Learning_rate: 0.0003\n",
      "Episode: 392, Total reward: -60.71897849215357, Average Rewards: -95.57069919554733, Epsilon: 0.74, Learning_rate: 0.0003\n",
      "Episode: 393, Total reward: -69.4057345842505, Average Rewards: -94.8289513626676, Epsilon: 0.74, Learning_rate: 0.0003\n",
      "Episode: 394, Total reward: -87.75289136785368, Average Rewards: -93.772762278798, Epsilon: 0.74, Learning_rate: 0.0003\n",
      "Episode: 395, Total reward: -8.104778692143455, Average Rewards: -92.91372539868827, Epsilon: 0.73, Learning_rate: 0.0003\n",
      "Episode: 396, Total reward: 11.669849075171726, Average Rewards: -93.16437872672063, Epsilon: 0.73, Learning_rate: 0.0003\n",
      "Episode: 397, Total reward: -115.86773202546493, Average Rewards: -92.12751373826663, Epsilon: 0.73, Learning_rate: 0.0003\n",
      "Episode: 398, Total reward: -13.84746779624139, Average Rewards: -90.69238718884486, Epsilon: 0.73, Learning_rate: 0.0003\n",
      "Episode: 399, Total reward: -83.82455234073635, Average Rewards: -88.9966120446449, Epsilon: 0.73, Learning_rate: 0.0003\n",
      "Episode: 400, Total reward: -39.238478509130275, Average Rewards: -88.78469977511776, Epsilon: 0.73, Learning_rate: 0.0003\n",
      "Episode: 401, Total reward: -125.74044109788039, Average Rewards: -88.42299125355012, Epsilon: 0.73, Learning_rate: 0.0003\n",
      "Episode: 402, Total reward: -49.12113496584328, Average Rewards: -88.28193432269234, Epsilon: 0.73, Learning_rate: 0.0003\n",
      "Episode: 403, Total reward: -62.810017954712926, Average Rewards: -87.81635159490934, Epsilon: 0.73, Learning_rate: 0.0003\n",
      "Episode: 404, Total reward: -110.05617808622307, Average Rewards: -87.86309688657215, Epsilon: 0.73, Learning_rate: 0.0003\n",
      "Episode: 405, Total reward: -283.9812797862038, Average Rewards: -88.3794014000796, Epsilon: 0.73, Learning_rate: 0.0003\n",
      "Episode: 406, Total reward: -86.35680944035326, Average Rewards: -90.23348796781458, Epsilon: 0.73, Learning_rate: 0.0003\n",
      "Episode: 407, Total reward: -29.092000281567636, Average Rewards: -90.86629268066305, Epsilon: 0.73, Learning_rate: 0.0003\n",
      "Episode: 408, Total reward: -56.347757426005124, Average Rewards: -89.91834179690048, Epsilon: 0.73, Learning_rate: 0.0003\n",
      "Episode: 409, Total reward: -72.44166251544809, Average Rewards: -88.70222414321258, Epsilon: 0.72, Learning_rate: 0.0003\n",
      "Episode: 410, Total reward: -52.030722878177016, Average Rewards: -86.9332135182228, Epsilon: 0.72, Learning_rate: 0.0003\n",
      "Episode: 411, Total reward: -86.76218584326308, Average Rewards: -87.11981134636606, Epsilon: 0.72, Learning_rate: 0.0003\n",
      "Episode: 412, Total reward: -89.18881684403397, Average Rewards: -86.16028667827516, Epsilon: 0.72, Learning_rate: 0.0003\n",
      "Episode: 413, Total reward: -73.07595432763013, Average Rewards: -86.01126544168626, Epsilon: 0.72, Learning_rate: 0.0003\n",
      "Episode: 414, Total reward: -28.1236069498937, Average Rewards: -85.72677696736095, Epsilon: 0.72, Learning_rate: 0.0003\n",
      "Episode: 415, Total reward: -85.85114238307324, Average Rewards: -84.84507919451723, Epsilon: 0.72, Learning_rate: 0.0003\n",
      "Episode: 416, Total reward: -72.96421092750226, Average Rewards: -82.77733102049186, Epsilon: 0.72, Learning_rate: 0.0003\n",
      "Episode: 417, Total reward: -60.073027253495844, Average Rewards: -82.98716041301887, Epsilon: 0.72, Learning_rate: 0.0003\n",
      "Episode: 418, Total reward: -52.61179471378662, Average Rewards: -83.39066539155459, Epsilon: 0.72, Learning_rate: 0.0003\n",
      "Episode: 419, Total reward: -106.19670514860412, Average Rewards: -82.99936255790354, Epsilon: 0.72, Learning_rate: 0.0003\n",
      "Episode: 420, Total reward: -93.89997419193568, Average Rewards: -82.87111015838376, Epsilon: 0.72, Learning_rate: 0.0003\n",
      "Episode: 421, Total reward: -61.6223283257109, Average Rewards: -83.10514396591617, Epsilon: 0.72, Learning_rate: 0.0003\n",
      "Episode: 422, Total reward: -82.16841651616427, Average Rewards: -82.55891362464021, Epsilon: 0.72, Learning_rate: 0.0003\n",
      "Episode: 423, Total reward: -41.62906640755689, Average Rewards: -82.55981986099805, Epsilon: 0.71, Learning_rate: 0.0003\n",
      "Episode: 424, Total reward: -87.85746484550812, Average Rewards: -82.60427655640576, Epsilon: 0.71, Learning_rate: 0.0003\n",
      "Episode: 425, Total reward: -47.96198502527327, Average Rewards: -82.70701620860753, Epsilon: 0.71, Learning_rate: 0.0003\n",
      "Episode: 426, Total reward: -103.7309459863139, Average Rewards: -82.12687085183052, Epsilon: 0.71, Learning_rate: 0.0003\n",
      "Episode: 427, Total reward: -91.63612348135057, Average Rewards: -82.8940611123673, Epsilon: 0.71, Learning_rate: 0.0003\n",
      "Episode: 428, Total reward: -34.763151422938805, Average Rewards: -82.7923190347795, Epsilon: 0.71, Learning_rate: 0.0003\n",
      "Episode: 429, Total reward: -82.63454989868187, Average Rewards: -82.45198360661152, Epsilon: 0.71, Learning_rate: 0.0003\n",
      "Episode: 430, Total reward: -86.64541940169164, Average Rewards: -82.60549517086896, Epsilon: 0.71, Learning_rate: 0.0003\n",
      "Episode: 431, Total reward: -91.57895036300485, Average Rewards: -82.1528512516021, Epsilon: 0.71, Learning_rate: 0.0003\n",
      "Episode: 432, Total reward: -33.04315094676586, Average Rewards: -82.30465164532957, Epsilon: 0.71, Learning_rate: 0.0003\n",
      "Episode: 433, Total reward: -38.27136686800945, Average Rewards: -81.67028927407696, Epsilon: 0.71, Learning_rate: 0.0003\n",
      "Episode: 434, Total reward: -52.13252199890967, Average Rewards: -80.85201842113933, Epsilon: 0.71, Learning_rate: 0.0003\n",
      "Episode: 435, Total reward: -123.85877392533246, Average Rewards: -80.63555170657445, Epsilon: 0.70, Learning_rate: 0.0003\n",
      "Episode: 436, Total reward: -82.0100360730332, Average Rewards: -80.8608166291115, Epsilon: 0.70, Learning_rate: 0.0003\n",
      "Episode: 437, Total reward: -2.869556370256319, Average Rewards: -80.16515700575246, Epsilon: 0.70, Learning_rate: 0.0003\n",
      "Episode: 438, Total reward: -82.65650275234202, Average Rewards: -79.83629175373987, Epsilon: 0.70, Learning_rate: 0.0003\n",
      "Episode: 439, Total reward: -67.01890937063627, Average Rewards: -79.83062117346317, Epsilon: 0.70, Learning_rate: 0.0003\n",
      "Episode: 440, Total reward: -86.27249859283013, Average Rewards: -79.80895393214426, Epsilon: 0.70, Learning_rate: 0.0003\n",
      "Episode: 441, Total reward: -49.04950378697623, Average Rewards: -81.48553847449652, Epsilon: 0.70, Learning_rate: 0.0003\n",
      "Episode: 442, Total reward: -56.08493552874228, Average Rewards: -81.01198340821352, Epsilon: 0.70, Learning_rate: 0.0003\n",
      "Episode: 443, Total reward: -90.9968489924758, Average Rewards: -80.64186945320948, Epsilon: 0.70, Learning_rate: 0.0003\n",
      "Episode: 444, Total reward: -78.34032642838096, Average Rewards: -80.8786885977763, Epsilon: 0.70, Learning_rate: 0.0003\n",
      "Episode: 445, Total reward: -48.16423383625393, Average Rewards: -80.60941420216454, Epsilon: 0.70, Learning_rate: 0.0003\n",
      "Episode: 446, Total reward: -41.20610771000807, Average Rewards: -80.70148989633242, Epsilon: 0.70, Learning_rate: 0.0003\n",
      "Episode: 447, Total reward: -24.658004024737323, Average Rewards: -80.03885379903642, Epsilon: 0.69, Learning_rate: 0.0003\n",
      "Episode: 448, Total reward: -111.97002711233831, Average Rewards: -79.1986080932768, Epsilon: 0.69, Learning_rate: 0.0003\n",
      "Episode: 449, Total reward: -87.86296056849815, Average Rewards: -79.71875452395511, Epsilon: 0.69, Learning_rate: 0.0003\n",
      "Episode: 450, Total reward: -137.78478864091153, Average Rewards: -79.08190845036684, Epsilon: 0.69, Learning_rate: 0.0003\n",
      "Episode: 451, Total reward: -45.40722646283055, Average Rewards: -79.33060764777557, Epsilon: 0.69, Learning_rate: 0.0003\n",
      "Episode: 452, Total reward: -71.13621296330842, Average Rewards: -78.41529861917036, Epsilon: 0.69, Learning_rate: 0.0003\n",
      "Episode: 453, Total reward: -97.05442351919856, Average Rewards: -78.96338932953748, Epsilon: 0.69, Learning_rate: 0.0003\n",
      "Episode: 454, Total reward: -178.6451983010339, Average Rewards: -79.51080977774787, Epsilon: 0.69, Learning_rate: 0.0003\n",
      "Episode: 455, Total reward: -79.6484479096065, Average Rewards: -80.31830652797156, Epsilon: 0.69, Learning_rate: 0.0003\n",
      "Episode: 456, Total reward: -82.1303254285718, Average Rewards: -79.98866968377808, Epsilon: 0.69, Learning_rate: 0.0003\n",
      "Episode: 457, Total reward: -38.86316813871199, Average Rewards: -79.58917438434132, Epsilon: 0.69, Learning_rate: 0.0003\n",
      "Episode: 458, Total reward: -50.93098602765829, Average Rewards: -79.02073200982403, Epsilon: 0.69, Learning_rate: 0.0003\n",
      "Episode: 459, Total reward: -25.764446831546366, Average Rewards: -78.92852826405942, Epsilon: 0.68, Learning_rate: 0.0003\n",
      "Episode: 460, Total reward: -43.25818519568621, Average Rewards: -78.25366531109496, Epsilon: 0.68, Learning_rate: 0.0003\n",
      "Episode: 461, Total reward: -67.46386569052632, Average Rewards: -77.8328252021032, Epsilon: 0.68, Learning_rate: 0.0003\n",
      "Episode: 462, Total reward: -76.73531068897356, Average Rewards: -78.07567512805947, Epsilon: 0.68, Learning_rate: 0.0003\n",
      "Episode: 463, Total reward: 34.40980687332706, Average Rewards: -77.31157384390171, Epsilon: 0.68, Learning_rate: 0.0003\n",
      "Episode: 464, Total reward: -123.07372268524318, Average Rewards: -75.62065329378449, Epsilon: 0.68, Learning_rate: 0.0003\n",
      "Episode: 465, Total reward: -77.83984005250855, Average Rewards: -76.28268158047496, Epsilon: 0.68, Learning_rate: 0.0003\n",
      "Episode: 466, Total reward: -52.81281065650183, Average Rewards: -75.65046807528778, Epsilon: 0.68, Learning_rate: 0.0003\n",
      "Episode: 467, Total reward: -68.80090557534103, Average Rewards: -75.47196899291262, Epsilon: 0.68, Learning_rate: 0.0003\n",
      "Episode: 468, Total reward: -32.20562067722035, Average Rewards: -75.41043701001927, Epsilon: 0.68, Learning_rate: 0.0003\n",
      "Episode: 469, Total reward: -209.73429151185354, Average Rewards: -74.99062032151018, Epsilon: 0.68, Learning_rate: 0.0003\n",
      "Episode: 470, Total reward: -119.21702773354048, Average Rewards: -76.56376080266101, Epsilon: 0.68, Learning_rate: 0.0003\n",
      "Episode: 471, Total reward: -89.66823999004309, Average Rewards: -77.27643193385032, Epsilon: 0.67, Learning_rate: 0.0003\n",
      "Episode: 472, Total reward: -104.44875744792917, Average Rewards: -77.64328133782153, Epsilon: 0.67, Learning_rate: 0.0003\n",
      "Episode: 473, Total reward: -61.50870999991892, Average Rewards: -78.03622892649247, Epsilon: 0.67, Learning_rate: 0.0003\n",
      "Episode: 474, Total reward: -112.12586795757355, Average Rewards: -77.73246099779197, Epsilon: 0.67, Learning_rate: 0.0003\n",
      "Episode: 475, Total reward: -45.3364866196368, Average Rewards: -78.1430914158687, Epsilon: 0.67, Learning_rate: 0.0003\n",
      "Episode: 476, Total reward: -62.17896605003917, Average Rewards: -77.88782653322717, Epsilon: 0.67, Learning_rate: 0.0003\n",
      "Episode: 477, Total reward: -47.05095566407286, Average Rewards: -77.04715724746245, Epsilon: 0.67, Learning_rate: 0.0003\n",
      "Episode: 478, Total reward: -63.10322272100835, Average Rewards: -76.93849667326172, Epsilon: 0.67, Learning_rate: 0.0003\n",
      "Episode: 479, Total reward: -85.16199135189581, Average Rewards: -75.36686692670898, Epsilon: 0.67, Learning_rate: 0.0003\n",
      "Episode: 480, Total reward: -76.89239880883447, Average Rewards: -76.01772434353556, Epsilon: 0.67, Learning_rate: 0.0003\n",
      "Episode: 481, Total reward: -102.10051653263797, Average Rewards: -76.14508654108646, Epsilon: 0.67, Learning_rate: 0.0003\n",
      "Episode: 482, Total reward: -101.39246614425937, Average Rewards: -76.47908921662811, Epsilon: 0.67, Learning_rate: 0.0003\n",
      "Episode: 483, Total reward: -84.62076300423863, Average Rewards: -76.67935270339727, Epsilon: 0.67, Learning_rate: 0.0003\n",
      "Episode: 484, Total reward: -116.30218474184367, Average Rewards: -76.35898765686663, Epsilon: 0.67, Learning_rate: 0.0003\n",
      "Episode: 485, Total reward: -86.6804521809122, Average Rewards: -76.71719955627864, Epsilon: 0.66, Learning_rate: 0.0003\n",
      "Episode: 486, Total reward: -89.63702888220597, Average Rewards: -75.55065168430883, Epsilon: 0.66, Learning_rate: 0.0003\n",
      "Episode: 487, Total reward: -196.95747296976668, Average Rewards: -74.75433095444053, Epsilon: 0.66, Learning_rate: 0.0003\n",
      "Episode: 488, Total reward: -109.60251951261628, Average Rewards: -76.1092125772767, Epsilon: 0.66, Learning_rate: 0.0003\n",
      "Episode: 489, Total reward: -43.88839364830194, Average Rewards: -76.03938748897754, Epsilon: 0.66, Learning_rate: 0.0003\n",
      "Episode: 490, Total reward: -68.23223392355659, Average Rewards: -75.62542422258907, Epsilon: 0.66, Learning_rate: 0.0003\n",
      "Episode: 491, Total reward: -19.495609489728608, Average Rewards: -75.35987173568887, Epsilon: 0.66, Learning_rate: 0.0003\n",
      "Episode: 492, Total reward: -111.59221552879765, Average Rewards: -75.22993089767624, Epsilon: 0.66, Learning_rate: 0.0003\n",
      "Episode: 493, Total reward: -116.77066832736169, Average Rewards: -75.73866326804269, Epsilon: 0.66, Learning_rate: 0.0003\n",
      "Episode: 494, Total reward: -59.089724733169064, Average Rewards: -76.2123126054738, Epsilon: 0.66, Learning_rate: 0.0003\n",
      "Episode: 495, Total reward: -114.94875183809827, Average Rewards: -75.92568093912695, Epsilon: 0.66, Learning_rate: 0.0003\n",
      "Episode: 496, Total reward: -57.00646059826089, Average Rewards: -76.99412067058648, Epsilon: 0.66, Learning_rate: 0.0003\n",
      "Episode: 497, Total reward: -74.02441288085367, Average Rewards: -77.68088376732081, Epsilon: 0.66, Learning_rate: 0.0003\n",
      "Episode: 498, Total reward: -48.369763439111225, Average Rewards: -77.2624505758747, Epsilon: 0.65, Learning_rate: 0.0003\n",
      "Episode: 499, Total reward: -5.2809538858984695, Average Rewards: -77.6076735323034, Epsilon: 0.65, Learning_rate: 0.0003\n",
      "Episode: 500, Total reward: -79.48533496170175, Average Rewards: -76.82223754775502, Epsilon: 0.65, Learning_rate: 0.0003\n",
      "Episode: 501, Total reward: -104.92355398985474, Average Rewards: -77.22470611228073, Epsilon: 0.65, Learning_rate: 0.0003\n",
      "Episode: 502, Total reward: -51.65390768429806, Average Rewards: -77.01653724120048, Epsilon: 0.65, Learning_rate: 0.0003\n",
      "Episode: 503, Total reward: -25.685791876808338, Average Rewards: -77.04186496838503, Epsilon: 0.65, Learning_rate: 0.0003\n",
      "Episode: 504, Total reward: -79.23895002085763, Average Rewards: -76.67062270760599, Epsilon: 0.65, Learning_rate: 0.0003\n",
      "Episode: 505, Total reward: -79.71174325980604, Average Rewards: -76.36245042695232, Epsilon: 0.65, Learning_rate: 0.0003\n",
      "Episode: 506, Total reward: -45.21628532013047, Average Rewards: -74.31975506168834, Epsilon: 0.65, Learning_rate: 0.0003\n",
      "Episode: 507, Total reward: -95.59816451993244, Average Rewards: -73.90834982048612, Epsilon: 0.65, Learning_rate: 0.0003\n",
      "Episode: 508, Total reward: -101.12586769230212, Average Rewards: -74.57341146286977, Epsilon: 0.65, Learning_rate: 0.0003\n",
      "Episode: 509, Total reward: -83.0087496496509, Average Rewards: -75.02119256553274, Epsilon: 0.64, Learning_rate: 0.0003\n",
      "Episode: 510, Total reward: -91.93565088562701, Average Rewards: -75.12686343687479, Epsilon: 0.64, Learning_rate: 0.0003\n",
      "Episode: 511, Total reward: 20.511770057508187, Average Rewards: -75.52591271694928, Epsilon: 0.64, Learning_rate: 0.0003\n",
      "Episode: 512, Total reward: -96.16877226677303, Average Rewards: -74.45317315794156, Epsilon: 0.64, Learning_rate: 0.0003\n",
      "Episode: 513, Total reward: -46.11687117631308, Average Rewards: -74.52297271216895, Epsilon: 0.64, Learning_rate: 0.0003\n",
      "Episode: 514, Total reward: -9.77536075265256, Average Rewards: -74.25338188065578, Epsilon: 0.64, Learning_rate: 0.0003\n",
      "Episode: 515, Total reward: -136.94296508650234, Average Rewards: -74.06989941868336, Epsilon: 0.64, Learning_rate: 0.0003\n",
      "Episode: 516, Total reward: -92.54460468486968, Average Rewards: -74.58081764571766, Epsilon: 0.64, Learning_rate: 0.0003\n",
      "Episode: 517, Total reward: -41.64809421466572, Average Rewards: -74.77662158329133, Epsilon: 0.64, Learning_rate: 0.0003\n",
      "Episode: 518, Total reward: -76.9190844557719, Average Rewards: -74.59237225290303, Epsilon: 0.64, Learning_rate: 0.0003\n",
      "Episode: 519, Total reward: -257.38343779521256, Average Rewards: -74.83544515032288, Epsilon: 0.64, Learning_rate: 0.0003\n",
      "Episode: 520, Total reward: -50.156364047281265, Average Rewards: -76.34731247678897, Epsilon: 0.64, Learning_rate: 0.0003\n",
      "Episode: 521, Total reward: -32.44435845427624, Average Rewards: -75.90987637534242, Epsilon: 0.64, Learning_rate: 0.0003\n",
      "Episode: 522, Total reward: -106.68699928175442, Average Rewards: -75.61809667662807, Epsilon: 0.63, Learning_rate: 0.0003\n",
      "Episode: 523, Total reward: -91.86919488318885, Average Rewards: -75.86328250428397, Epsilon: 0.63, Learning_rate: 0.0003\n",
      "Episode: 524, Total reward: -30.84088362411056, Average Rewards: -76.3656837890403, Epsilon: 0.63, Learning_rate: 0.0003\n",
      "Episode: 525, Total reward: -42.449776190276026, Average Rewards: -75.79551797682632, Epsilon: 0.63, Learning_rate: 0.0003\n",
      "Episode: 526, Total reward: -67.50171404828392, Average Rewards: -75.74039588847634, Epsilon: 0.63, Learning_rate: 0.0003\n",
      "Episode: 527, Total reward: -30.922178815364944, Average Rewards: -75.37810356909606, Epsilon: 0.63, Learning_rate: 0.0003\n",
      "Episode: 528, Total reward: -44.72643109504566, Average Rewards: -74.77096412243618, Epsilon: 0.63, Learning_rate: 0.0003\n",
      "Episode: 529, Total reward: -86.20359861317098, Average Rewards: -74.87059691915726, Epsilon: 0.63, Learning_rate: 0.0003\n",
      "Episode: 530, Total reward: -38.194949449795274, Average Rewards: -74.90628740630216, Epsilon: 0.63, Learning_rate: 0.0003\n",
      "Episode: 531, Total reward: -56.92399054272899, Average Rewards: -74.42178270678318, Epsilon: 0.63, Learning_rate: 0.0003\n",
      "Episode: 532, Total reward: -142.23169824269362, Average Rewards: -74.07523310858043, Epsilon: 0.63, Learning_rate: 0.0003\n",
      "Episode: 533, Total reward: -42.31011877431911, Average Rewards: -75.16711858153971, Epsilon: 0.62, Learning_rate: 0.0003\n",
      "Episode: 534, Total reward: -103.49578699949102, Average Rewards: -75.2075061006028, Epsilon: 0.62, Learning_rate: 0.0003\n",
      "Episode: 535, Total reward: -34.85252111529037, Average Rewards: -75.72113875060862, Epsilon: 0.62, Learning_rate: 0.0003\n",
      "Episode: 536, Total reward: -105.76488196764818, Average Rewards: -74.83107622250819, Epsilon: 0.62, Learning_rate: 0.0003\n",
      "Episode: 537, Total reward: -96.84639632101272, Average Rewards: -75.06862468145435, Epsilon: 0.62, Learning_rate: 0.0003\n",
      "Episode: 538, Total reward: -44.862854184955424, Average Rewards: -76.00839308096191, Epsilon: 0.62, Learning_rate: 0.0003\n",
      "Episode: 539, Total reward: -73.4356569562618, Average Rewards: -75.63045659528804, Epsilon: 0.62, Learning_rate: 0.0003\n",
      "Episode: 540, Total reward: -123.99768439741955, Average Rewards: -75.69462407114429, Epsilon: 0.62, Learning_rate: 0.0003\n",
      "Episode: 541, Total reward: -30.55063676591098, Average Rewards: -76.07187592919018, Epsilon: 0.62, Learning_rate: 0.0003\n",
      "Episode: 542, Total reward: -212.40380035122968, Average Rewards: -75.88688725897953, Epsilon: 0.62, Learning_rate: 0.0003\n",
      "Episode: 543, Total reward: -115.54387312876078, Average Rewards: -77.4500759072044, Epsilon: 0.62, Learning_rate: 0.0003\n",
      "Episode: 544, Total reward: -76.25832057806889, Average Rewards: -77.69554614856726, Epsilon: 0.62, Learning_rate: 0.0003\n",
      "Episode: 545, Total reward: -6.599468901165395, Average Rewards: -77.67472609006414, Epsilon: 0.61, Learning_rate: 0.0003\n",
      "Episode: 546, Total reward: -81.81421023422475, Average Rewards: -77.25907844071325, Epsilon: 0.61, Learning_rate: 0.0003\n",
      "Episode: 547, Total reward: -93.79664159381659, Average Rewards: -77.66515946595541, Epsilon: 0.61, Learning_rate: 0.0003\n",
      "Episode: 548, Total reward: -19.197933612567127, Average Rewards: -78.35654584164621, Epsilon: 0.61, Learning_rate: 0.0003\n",
      "Episode: 549, Total reward: -49.52227452669186, Average Rewards: -77.42882490664849, Epsilon: 0.61, Learning_rate: 0.0003\n",
      "Episode: 550, Total reward: -68.2211881286281, Average Rewards: -77.04541804623044, Epsilon: 0.61, Learning_rate: 0.0003\n",
      "Episode: 551, Total reward: -108.64184251329557, Average Rewards: -76.3497820411076, Epsilon: 0.61, Learning_rate: 0.0003\n",
      "Episode: 552, Total reward: -51.2880053559963, Average Rewards: -76.98212820161226, Epsilon: 0.61, Learning_rate: 0.0003\n",
      "Episode: 553, Total reward: -60.415933499461964, Average Rewards: -76.78364612553914, Epsilon: 0.61, Learning_rate: 0.0003\n",
      "Episode: 554, Total reward: -121.1582892175107, Average Rewards: -76.41726122534178, Epsilon: 0.61, Learning_rate: 0.0003\n",
      "Episode: 555, Total reward: -54.37981920142576, Average Rewards: -75.84239213450654, Epsilon: 0.61, Learning_rate: 0.0003\n",
      "Episode: 556, Total reward: -91.07493394064029, Average Rewards: -75.58970584742472, Epsilon: 0.61, Learning_rate: 0.0003\n",
      "Episode: 557, Total reward: -199.73581333614757, Average Rewards: -75.67915193254541, Epsilon: 0.61, Learning_rate: 0.0003\n",
      "Episode: 558, Total reward: -126.02800294103845, Average Rewards: -77.28787838451977, Epsilon: 0.60, Learning_rate: 0.0003\n",
      "Episode: 559, Total reward: -0.7521377584489954, Average Rewards: -78.03884855365357, Epsilon: 0.60, Learning_rate: 0.0003\n",
      "Episode: 560, Total reward: -59.789963875774845, Average Rewards: -77.7887254629226, Epsilon: 0.60, Learning_rate: 0.0003\n",
      "Episode: 561, Total reward: -10.914797387395382, Average Rewards: -77.9540432497235, Epsilon: 0.60, Learning_rate: 0.0003\n",
      "Episode: 562, Total reward: -82.17951126167138, Average Rewards: -77.38855256669217, Epsilon: 0.60, Learning_rate: 0.0003\n",
      "Episode: 563, Total reward: -98.86270137723608, Average Rewards: -77.44299457241915, Epsilon: 0.60, Learning_rate: 0.0003\n",
      "Episode: 564, Total reward: -78.31117377912081, Average Rewards: -78.77571965492477, Epsilon: 0.60, Learning_rate: 0.0003\n",
      "Episode: 565, Total reward: -165.09615657870086, Average Rewards: -78.32809416586355, Epsilon: 0.60, Learning_rate: 0.0003\n",
      "Episode: 566, Total reward: -15.115686639146503, Average Rewards: -79.20065733112547, Epsilon: 0.60, Learning_rate: 0.0003\n",
      "Episode: 567, Total reward: -80.97251349818052, Average Rewards: -78.82368609095192, Epsilon: 0.60, Learning_rate: 0.0003\n",
      "Episode: 568, Total reward: -53.61925645965909, Average Rewards: -78.94540217018033, Epsilon: 0.60, Learning_rate: 0.0003\n",
      "Episode: 569, Total reward: -113.14475984274317, Average Rewards: -79.15953852800472, Epsilon: 0.60, Learning_rate: 0.0003\n",
      "Episode: 570, Total reward: -114.99308142250443, Average Rewards: -78.19364321131361, Epsilon: 0.59, Learning_rate: 0.0003\n",
      "Episode: 571, Total reward: -68.43840102594359, Average Rewards: -78.15140374820325, Epsilon: 0.59, Learning_rate: 0.0003\n",
      "Episode: 572, Total reward: -26.641081573919877, Average Rewards: -77.93910535856226, Epsilon: 0.59, Learning_rate: 0.0003\n",
      "Episode: 573, Total reward: -123.82787924709268, Average Rewards: -77.16102859982216, Epsilon: 0.59, Learning_rate: 0.0003\n",
      "Episode: 574, Total reward: -3.167277613892935, Average Rewards: -77.78422029229391, Epsilon: 0.59, Learning_rate: 0.0003\n",
      "Episode: 575, Total reward: -63.76667648530377, Average Rewards: -76.6946343888571, Epsilon: 0.59, Learning_rate: 0.0003\n",
      "Episode: 576, Total reward: -81.00212952175494, Average Rewards: -76.87893628751377, Epsilon: 0.59, Learning_rate: 0.0003\n",
      "Episode: 577, Total reward: -115.64591602625579, Average Rewards: -77.06716792223092, Epsilon: 0.59, Learning_rate: 0.0003\n",
      "Episode: 578, Total reward: -51.70010981317779, Average Rewards: -77.75311752585276, Epsilon: 0.59, Learning_rate: 0.0003\n",
      "Episode: 579, Total reward: -41.22434239856986, Average Rewards: -77.63908639677445, Epsilon: 0.59, Learning_rate: 0.0003\n",
      "Episode: 580, Total reward: -203.69996189531724, Average Rewards: -77.19970990724119, Epsilon: 0.58, Learning_rate: 0.0003\n",
      "Episode: 581, Total reward: -60.048113278127886, Average Rewards: -78.46778553810601, Epsilon: 0.58, Learning_rate: 0.0003\n",
      "Episode: 582, Total reward: -94.57889310400411, Average Rewards: -78.04726150556091, Epsilon: 0.58, Learning_rate: 0.0003\n",
      "Episode: 583, Total reward: -83.87700000687025, Average Rewards: -77.97912577515837, Epsilon: 0.58, Learning_rate: 0.0003\n",
      "Episode: 584, Total reward: -46.2286216930583, Average Rewards: -77.97168814518467, Epsilon: 0.58, Learning_rate: 0.0003\n",
      "Episode: 585, Total reward: -61.738264331272546, Average Rewards: -77.27095251469682, Epsilon: 0.58, Learning_rate: 0.0003\n",
      "Episode: 586, Total reward: -14.406780152305885, Average Rewards: -77.02153063620042, Epsilon: 0.58, Learning_rate: 0.0003\n",
      "Episode: 587, Total reward: -65.43042770041936, Average Rewards: -76.26922814890142, Epsilon: 0.58, Learning_rate: 0.0003\n",
      "Episode: 588, Total reward: -54.56643147804333, Average Rewards: -74.95395769620795, Epsilon: 0.58, Learning_rate: 0.0003\n",
      "Episode: 589, Total reward: -76.42190347789773, Average Rewards: -74.40359681586222, Epsilon: 0.58, Learning_rate: 0.0003\n",
      "Episode: 590, Total reward: -0.8735728572712702, Average Rewards: -74.72893191415818, Epsilon: 0.58, Learning_rate: 0.0003\n",
      "Episode: 591, Total reward: -87.58038069746416, Average Rewards: -74.05534530349533, Epsilon: 0.57, Learning_rate: 0.0003\n",
      "Episode: 592, Total reward: -46.58037116310818, Average Rewards: -74.7361930155727, Epsilon: 0.57, Learning_rate: 0.0003\n",
      "Episode: 593, Total reward: -161.18726244274382, Average Rewards: -74.08607457191579, Epsilon: 0.57, Learning_rate: 0.0003\n",
      "Episode: 594, Total reward: -65.22122494587995, Average Rewards: -74.53024051306961, Epsilon: 0.57, Learning_rate: 0.0003\n",
      "Episode: 595, Total reward: -43.2782715258258, Average Rewards: -74.59155551519672, Epsilon: 0.57, Learning_rate: 0.0003\n",
      "Episode: 596, Total reward: -79.33560985927792, Average Rewards: -73.87485071207398, Epsilon: 0.57, Learning_rate: 0.0003\n",
      "Episode: 597, Total reward: -82.33181119335283, Average Rewards: -74.09814220468415, Epsilon: 0.57, Learning_rate: 0.0003\n",
      "Episode: 598, Total reward: -208.9059377556822, Average Rewards: -74.18121618780916, Epsilon: 0.57, Learning_rate: 0.0003\n",
      "Episode: 599, Total reward: -27.091304091430587, Average Rewards: -75.78657793097486, Epsilon: 0.57, Learning_rate: 0.0003\n",
      "Episode: 600, Total reward: -32.55581619828958, Average Rewards: -76.00468143303019, Epsilon: 0.57, Learning_rate: 0.0003\n",
      "Episode: 601, Total reward: -61.517780863815155, Average Rewards: -75.53538624539607, Epsilon: 0.57, Learning_rate: 0.0003\n",
      "Episode: 602, Total reward: -51.30123032790395, Average Rewards: -75.10132851413567, Epsilon: 0.56, Learning_rate: 0.0003\n",
      "Episode: 603, Total reward: 8.289589733278348, Average Rewards: -75.09780174057173, Epsilon: 0.56, Learning_rate: 0.0003\n",
      "Episode: 604, Total reward: -73.606131063301, Average Rewards: -74.75804792447087, Epsilon: 0.56, Learning_rate: 0.0003\n",
      "Episode: 605, Total reward: 25.580578312731248, Average Rewards: -74.7017197348953, Epsilon: 0.56, Learning_rate: 0.0003\n",
      "Episode: 606, Total reward: -44.945857480290115, Average Rewards: -73.64879651916992, Epsilon: 0.56, Learning_rate: 0.0003\n",
      "Episode: 607, Total reward: -184.5157750356506, Average Rewards: -73.64609224077151, Epsilon: 0.56, Learning_rate: 0.0003\n",
      "Episode: 608, Total reward: -145.92700426574632, Average Rewards: -74.5352683459287, Epsilon: 0.56, Learning_rate: 0.0003\n",
      "Episode: 609, Total reward: -50.891019491822206, Average Rewards: -74.98327971166313, Epsilon: 0.56, Learning_rate: 0.0003\n",
      "Episode: 610, Total reward: -149.054515013978, Average Rewards: -74.66210241008486, Epsilon: 0.55, Learning_rate: 0.0003\n",
      "Episode: 611, Total reward: -18.683849469325196, Average Rewards: -75.23329105136835, Epsilon: 0.55, Learning_rate: 0.0003\n",
      "Episode: 612, Total reward: 10.001862542346686, Average Rewards: -75.6252472466367, Epsilon: 0.54, Learning_rate: 0.0003\n",
      "Episode: 613, Total reward: -94.09328083794968, Average Rewards: -74.5635408985455, Epsilon: 0.54, Learning_rate: 0.0003\n",
      "Episode: 614, Total reward: -85.03850709987265, Average Rewards: -75.04330499516186, Epsilon: 0.54, Learning_rate: 0.0003\n",
      "Episode: 615, Total reward: 10.429164379370746, Average Rewards: -75.79593645863407, Epsilon: 0.54, Learning_rate: 0.0003\n",
      "Episode: 616, Total reward: -67.2882496904927, Average Rewards: -74.32221516397534, Epsilon: 0.54, Learning_rate: 0.0003\n",
      "Episode: 617, Total reward: 3.292499354161805, Average Rewards: -74.06965161403156, Epsilon: 0.54, Learning_rate: 0.0003\n",
      "Episode: 618, Total reward: -101.84136245893313, Average Rewards: -73.6202456783433, Epsilon: 0.54, Learning_rate: 0.0003\n",
      "Episode: 619, Total reward: 6.295211828301817, Average Rewards: -73.86946845837491, Epsilon: 0.54, Learning_rate: 0.0003\n",
      "Episode: 620, Total reward: -190.78489775425868, Average Rewards: -71.23268196213976, Epsilon: 0.54, Learning_rate: 0.0003\n",
      "Episode: 621, Total reward: -57.51228133983153, Average Rewards: -72.63896729920954, Epsilon: 0.53, Learning_rate: 0.0003\n",
      "Episode: 622, Total reward: -19.603338377477556, Average Rewards: -72.88964652806507, Epsilon: 0.53, Learning_rate: 0.0003\n",
      "Episode: 623, Total reward: -106.33405265418236, Average Rewards: -72.0188099190223, Epsilon: 0.53, Learning_rate: 0.0003\n",
      "Episode: 624, Total reward: -15.606724630754982, Average Rewards: -72.16345849673225, Epsilon: 0.53, Learning_rate: 0.0003\n",
      "Episode: 625, Total reward: -54.75954878977467, Average Rewards: -72.01111690679869, Epsilon: 0.53, Learning_rate: 0.0003\n",
      "Episode: 626, Total reward: -69.19184162350633, Average Rewards: -72.13421463279367, Epsilon: 0.53, Learning_rate: 0.0003\n",
      "Episode: 627, Total reward: -37.698705266512405, Average Rewards: -72.1511159085459, Epsilon: 0.53, Learning_rate: 0.0003\n",
      "Episode: 628, Total reward: -71.69602803825717, Average Rewards: -72.21888117305738, Epsilon: 0.52, Learning_rate: 0.0003\n",
      "Episode: 629, Total reward: -114.23499828984232, Average Rewards: -72.48857714248949, Epsilon: 0.52, Learning_rate: 0.0003\n",
      "Episode: 630, Total reward: -14.65753317087102, Average Rewards: -72.76889113925623, Epsilon: 0.52, Learning_rate: 0.0003\n",
      "Episode: 631, Total reward: -74.64045889361587, Average Rewards: -72.53351697646696, Epsilon: 0.52, Learning_rate: 0.0003\n",
      "Episode: 632, Total reward: -292.2221600015363, Average Rewards: -72.71068165997583, Epsilon: 0.52, Learning_rate: 0.0003\n",
      "Episode: 633, Total reward: -49.156908547140816, Average Rewards: -74.21058627756426, Epsilon: 0.52, Learning_rate: 0.0003\n",
      "Episode: 634, Total reward: -66.39180719977743, Average Rewards: -74.27905417529249, Epsilon: 0.52, Learning_rate: 0.0003\n",
      "Episode: 635, Total reward: -279.5418735279387, Average Rewards: -73.90801437729534, Epsilon: 0.52, Learning_rate: 0.0003\n",
      "Episode: 636, Total reward: -32.505414491589775, Average Rewards: -76.35490790142184, Epsilon: 0.51, Learning_rate: 0.0003\n",
      "Episode: 637, Total reward: -117.09634177252431, Average Rewards: -75.62231322666125, Epsilon: 0.51, Learning_rate: 0.0003\n",
      "Episode: 638, Total reward: -99.81247867225163, Average Rewards: -75.82481268117637, Epsilon: 0.51, Learning_rate: 0.0003\n",
      "Episode: 639, Total reward: 33.14757533039739, Average Rewards: -76.37430892604932, Epsilon: 0.51, Learning_rate: 0.0003\n",
      "Episode: 640, Total reward: -189.03670050849746, Average Rewards: -75.30847660318273, Epsilon: 0.51, Learning_rate: 0.0003\n",
      "Episode: 641, Total reward: -48.193198265165336, Average Rewards: -75.9588667642935, Epsilon: 0.51, Learning_rate: 0.0003\n",
      "Episode: 642, Total reward: -223.665084047355, Average Rewards: -76.13529237928604, Epsilon: 0.50, Learning_rate: 0.0003\n",
      "Episode: 643, Total reward: -75.68416420891033, Average Rewards: -76.24790521624729, Epsilon: 0.50, Learning_rate: 0.0003\n",
      "Episode: 644, Total reward: -109.5157767213555, Average Rewards: -75.84930812704879, Epsilon: 0.50, Learning_rate: 0.0003\n",
      "Episode: 645, Total reward: -48.2163873492257, Average Rewards: -76.18188268848166, Epsilon: 0.50, Learning_rate: 0.0003\n",
      "Episode: 646, Total reward: -41.18868719406416, Average Rewards: -76.59805187296226, Epsilon: 0.50, Learning_rate: 0.0003\n",
      "Episode: 647, Total reward: -190.5887533240304, Average Rewards: -76.19179664256066, Epsilon: 0.50, Learning_rate: 0.0003\n",
      "Episode: 648, Total reward: -59.55678916675974, Average Rewards: -77.1597177598628, Epsilon: 0.49, Learning_rate: 0.0003\n",
      "Episode: 649, Total reward: 50.60129849043016, Average Rewards: -77.56330631540473, Epsilon: 0.48, Learning_rate: 0.0003\n",
      "Episode: 650, Total reward: -18.056845215814775, Average Rewards: -76.5620705852335, Epsilon: 0.48, Learning_rate: 0.0003\n",
      "Episode: 651, Total reward: -15.601423474063836, Average Rewards: -76.06042715610538, Epsilon: 0.48, Learning_rate: 0.0003\n",
      "Episode: 652, Total reward: 11.174761199543255, Average Rewards: -75.13002296571307, Epsilon: 0.48, Learning_rate: 0.0003\n",
      "Episode: 653, Total reward: -38.95080396051456, Average Rewards: -74.50539530015767, Epsilon: 0.48, Learning_rate: 0.0003\n",
      "Episode: 654, Total reward: -23.305238430854914, Average Rewards: -74.2907440047682, Epsilon: 0.48, Learning_rate: 0.0003\n",
      "Episode: 655, Total reward: -76.20765509371789, Average Rewards: -73.31221349690163, Epsilon: 0.48, Learning_rate: 0.0003\n",
      "Episode: 656, Total reward: -34.14229826449436, Average Rewards: -73.53049185582455, Epsilon: 0.48, Learning_rate: 0.0003\n",
      "Episode: 657, Total reward: 49.38232734831752, Average Rewards: -72.9611654990631, Epsilon: 0.47, Learning_rate: 0.0003\n",
      "Episode: 658, Total reward: -14.341122779338889, Average Rewards: -70.46998409221845, Epsilon: 0.47, Learning_rate: 0.0003\n",
      "Episode: 659, Total reward: -117.14695073482953, Average Rewards: -69.35311529060145, Epsilon: 0.47, Learning_rate: 0.0003\n",
      "Episode: 660, Total reward: -57.03446630321174, Average Rewards: -70.51706342036525, Epsilon: 0.47, Learning_rate: 0.0003\n",
      "Episode: 661, Total reward: -18.246739232910173, Average Rewards: -70.48950844463963, Epsilon: 0.47, Learning_rate: 0.0003\n",
      "Episode: 662, Total reward: -23.76298868289352, Average Rewards: -70.56282786309477, Epsilon: 0.47, Learning_rate: 0.0003\n",
      "Episode: 663, Total reward: -15.215412591473083, Average Rewards: -69.978662637307, Epsilon: 0.46, Learning_rate: 0.0003\n",
      "Episode: 664, Total reward: -88.89755585094596, Average Rewards: -69.14218974944936, Epsilon: 0.46, Learning_rate: 0.0003\n",
      "Episode: 665, Total reward: -57.22201903993775, Average Rewards: -69.2480535701676, Epsilon: 0.46, Learning_rate: 0.0003\n",
      "Episode: 666, Total reward: -101.79532706133342, Average Rewards: -68.16931219477996, Epsilon: 0.46, Learning_rate: 0.0003\n",
      "Episode: 667, Total reward: -32.29655606115594, Average Rewards: -69.03610859900185, Epsilon: 0.46, Learning_rate: 0.0003\n",
      "Episode: 668, Total reward: 30.04264523598195, Average Rewards: -68.5493490246316, Epsilon: 0.46, Learning_rate: 0.0003\n",
      "Episode: 669, Total reward: -176.38870900784494, Average Rewards: -67.71273000767519, Epsilon: 0.45, Learning_rate: 0.0003\n",
      "Episode: 670, Total reward: -68.82047986459101, Average Rewards: -68.34516949932622, Epsilon: 0.45, Learning_rate: 0.0003\n",
      "Episode: 671, Total reward: -43.43015036473585, Average Rewards: -67.88344348374707, Epsilon: 0.45, Learning_rate: 0.0003\n",
      "Episode: 672, Total reward: -78.79676488306848, Average Rewards: -67.63336097713501, Epsilon: 0.45, Learning_rate: 0.0003\n",
      "Episode: 673, Total reward: -160.72307240915046, Average Rewards: -68.1549178102265, Epsilon: 0.44, Learning_rate: 0.0003\n",
      "Episode: 674, Total reward: -85.8271718395996, Average Rewards: -68.52386974184707, Epsilon: 0.44, Learning_rate: 0.0003\n",
      "Episode: 675, Total reward: -142.1426850748806, Average Rewards: -69.35046868410413, Epsilon: 0.43, Learning_rate: 0.0003\n",
      "Episode: 676, Total reward: -16.674005758921638, Average Rewards: -70.1342287699999, Epsilon: 0.43, Learning_rate: 0.0003\n",
      "Episode: 677, Total reward: -210.2572532244668, Average Rewards: -69.49094753237156, Epsilon: 0.43, Learning_rate: 0.0003\n",
      "Episode: 678, Total reward: -60.45828197824999, Average Rewards: -70.43706090435367, Epsilon: 0.43, Learning_rate: 0.0003\n",
      "Episode: 679, Total reward: -89.64015210182443, Average Rewards: -70.5246426260044, Epsilon: 0.42, Learning_rate: 0.0003\n",
      "Episode: 680, Total reward: -46.63505841633787, Average Rewards: -71.00880072303694, Epsilon: 0.42, Learning_rate: 0.0003\n",
      "Episode: 681, Total reward: -53.43060936272718, Average Rewards: -69.43815168824715, Epsilon: 0.41, Learning_rate: 0.0003\n",
      "Episode: 682, Total reward: -66.55961616939078, Average Rewards: -69.37197664909314, Epsilon: 0.41, Learning_rate: 0.0003\n",
      "Episode: 683, Total reward: -55.253711618442814, Average Rewards: -69.09178387974701, Epsilon: 0.41, Learning_rate: 0.0003\n",
      "Episode: 684, Total reward: -48.13559726747856, Average Rewards: -68.80555099586275, Epsilon: 0.41, Learning_rate: 0.0003\n",
      "Episode: 685, Total reward: -324.96870756687076, Average Rewards: -68.82462075160694, Epsilon: 0.40, Learning_rate: 0.0003\n",
      "Episode: 686, Total reward: -213.76529673588666, Average Rewards: -71.45692518396292, Epsilon: 0.40, Learning_rate: 0.0003\n",
      "Episode: 687, Total reward: 56.014709430436625, Average Rewards: -73.45051034979872, Epsilon: 0.39, Learning_rate: 0.0003\n",
      "Episode: 688, Total reward: -103.03778496192155, Average Rewards: -72.23605897849016, Epsilon: 0.39, Learning_rate: 0.0003\n",
      "Episode: 689, Total reward: -136.32836902468887, Average Rewards: -72.72077251332894, Epsilon: 0.38, Learning_rate: 0.0003\n",
      "Episode: 690, Total reward: -80.47762290415234, Average Rewards: -73.31983716879685, Epsilon: 0.37, Learning_rate: 0.0003\n",
      "Episode: 691, Total reward: -92.27049223676181, Average Rewards: -74.11587766926567, Epsilon: 0.37, Learning_rate: 0.0003\n",
      "Episode: 692, Total reward: -77.96567101272153, Average Rewards: -74.16277878465866, Epsilon: 0.36, Learning_rate: 0.0003\n",
      "Episode: 693, Total reward: -10.946482825374346, Average Rewards: -74.47663178315479, Epsilon: 0.36, Learning_rate: 0.0003\n",
      "Episode: 694, Total reward: -17.701540377578716, Average Rewards: -72.97422398698109, Epsilon: 0.35, Learning_rate: 0.0003\n",
      "Episode: 695, Total reward: -29.899464411104674, Average Rewards: -72.49902714129807, Epsilon: 0.35, Learning_rate: 0.0003\n",
      "Episode: 696, Total reward: -2.0880537452921155, Average Rewards: -72.36523907015085, Epsilon: 0.34, Learning_rate: 0.0003\n",
      "Episode: 697, Total reward: -17.016352599524883, Average Rewards: -71.592763509011, Epsilon: 0.33, Learning_rate: 0.0003\n",
      "Episode: 698, Total reward: -36.27778930659789, Average Rewards: -70.93960892307273, Epsilon: 0.33, Learning_rate: 0.0003\n",
      "Episode: 699, Total reward: -4.0966834404164825, Average Rewards: -69.21332743858187, Epsilon: 0.32, Learning_rate: 0.0003\n",
      "Episode: 700, Total reward: -15.440988833639471, Average Rewards: -68.98338123207174, Epsilon: 0.31, Learning_rate: 0.0003\n",
      "Episode: 701, Total reward: -35.85266175995276, Average Rewards: -68.81223295842524, Epsilon: 0.31, Learning_rate: 0.0003\n",
      "Episode: 702, Total reward: -20.906047007401263, Average Rewards: -68.55558176738661, Epsilon: 0.30, Learning_rate: 0.0003\n",
      "Episode: 703, Total reward: -8.595102440711944, Average Rewards: -68.25162993418158, Epsilon: 0.29, Learning_rate: 0.0003\n",
      "Episode: 704, Total reward: -51.99918294719539, Average Rewards: -68.42047685592149, Epsilon: 0.29, Learning_rate: 0.0003\n",
      "Episode: 705, Total reward: -68.63101887884535, Average Rewards: -68.20440737476044, Epsilon: 0.28, Learning_rate: 0.0003\n",
      "Episode: 706, Total reward: -20.558611205276065, Average Rewards: -69.1465233466762, Epsilon: 0.28, Learning_rate: 0.0003\n",
      "Episode: 707, Total reward: -85.94623499886028, Average Rewards: -68.90265088392607, Epsilon: 0.27, Learning_rate: 0.0003\n",
      "Episode: 708, Total reward: -48.209489110684274, Average Rewards: -67.91695548355816, Epsilon: 0.26, Learning_rate: 0.0003\n",
      "Episode: 709, Total reward: -41.3586190813738, Average Rewards: -66.93978033200753, Epsilon: 0.26, Learning_rate: 0.0003\n",
      "Episode: 710, Total reward: -16.980379112247416, Average Rewards: -66.84445632790306, Epsilon: 0.25, Learning_rate: 0.0003\n",
      "Episode: 711, Total reward: -57.47092219676715, Average Rewards: -65.52371496888576, Epsilon: 0.24, Learning_rate: 0.0003\n",
      "Episode: 712, Total reward: 0.33091154271873996, Average Rewards: -65.91158569616017, Epsilon: 0.24, Learning_rate: 0.0003\n",
      "Episode: 713, Total reward: -22.713171373772603, Average Rewards: -66.00829520615645, Epsilon: 0.23, Learning_rate: 0.0003\n",
      "Episode: 714, Total reward: -119.88293203658468, Average Rewards: -65.29449411151468, Epsilon: 0.23, Learning_rate: 0.0003\n",
      "Episode: 715, Total reward: -46.407946104416084, Average Rewards: -65.64293836088179, Epsilon: 0.22, Learning_rate: 0.0003\n",
      "Episode: 716, Total reward: -32.25391011958668, Average Rewards: -66.21130946571967, Epsilon: 0.22, Learning_rate: 0.0003\n",
      "Episode: 717, Total reward: -52.66921126980566, Average Rewards: -65.8609660700106, Epsilon: 0.21, Learning_rate: 0.0003\n",
      "Episode: 718, Total reward: -107.19348791413215, Average Rewards: -66.42058317625029, Epsilon: 0.21, Learning_rate: 0.0003\n",
      "Episode: 719, Total reward: -39.845893015416536, Average Rewards: -66.47410443080226, Epsilon: 0.20, Learning_rate: 0.0003\n",
      "Episode: 720, Total reward: -15.501403609293689, Average Rewards: -66.93551547923944, Epsilon: 0.19, Learning_rate: 0.0003\n",
      "Episode: 721, Total reward: -119.89359108970554, Average Rewards: -65.18268053778979, Epsilon: 0.19, Learning_rate: 0.0003\n",
      "Episode: 722, Total reward: -62.24299455294361, Average Rewards: -65.80649363528853, Epsilon: 0.18, Learning_rate: 0.0003\n",
      "Episode: 723, Total reward: -50.16860162761853, Average Rewards: -66.2328901970432, Epsilon: 0.18, Learning_rate: 0.0003\n",
      "Episode: 724, Total reward: -42.05215385317837, Average Rewards: -65.67123568677756, Epsilon: 0.17, Learning_rate: 0.0003\n",
      "Episode: 725, Total reward: -57.66352120621873, Average Rewards: -65.9356899790018, Epsilon: 0.16, Learning_rate: 0.0003\n",
      "Episode: 726, Total reward: -45.01346593075197, Average Rewards: -65.96472970316624, Epsilon: 0.16, Learning_rate: 0.0003\n",
      "Episode: 727, Total reward: -44.48934004359323, Average Rewards: -65.7229459462387, Epsilon: 0.15, Learning_rate: 0.0003\n",
      "Episode: 728, Total reward: -116.88483480002321, Average Rewards: -65.7908522940095, Epsilon: 0.15, Learning_rate: 0.0003\n",
      "Episode: 729, Total reward: -44.528240362234776, Average Rewards: -66.24274036162716, Epsilon: 0.14, Learning_rate: 0.0003\n",
      "Episode: 730, Total reward: -74.8120812859073, Average Rewards: -65.54567278235109, Epsilon: 0.14, Learning_rate: 0.0003\n",
      "Episode: 731, Total reward: -38.120478573318834, Average Rewards: -66.14721826350144, Epsilon: 0.13, Learning_rate: 0.0003\n",
      "Episode: 732, Total reward: -50.21730645319489, Average Rewards: -65.78201846029847, Epsilon: 0.12, Learning_rate: 0.0003\n",
      "Episode: 733, Total reward: -101.59174920890874, Average Rewards: -63.36196992481508, Epsilon: 0.12, Learning_rate: 0.0003\n",
      "Episode: 734, Total reward: -52.25939571156622, Average Rewards: -63.88631833143275, Epsilon: 0.12, Learning_rate: 0.0003\n",
      "Episode: 735, Total reward: -195.8823570995936, Average Rewards: -63.74499421655063, Epsilon: 0.11, Learning_rate: 0.0003\n",
      "Episode: 736, Total reward: -24.67972057370606, Average Rewards: -62.908399052267185, Epsilon: 0.11, Learning_rate: 0.0003\n",
      "Episode: 737, Total reward: -21.01416220299831, Average Rewards: -62.83014211308834, Epsilon: 0.10, Learning_rate: 0.0003\n",
      "Episode: 738, Total reward: -217.1490375156131, Average Rewards: -61.86932031739308, Epsilon: 0.09, Learning_rate: 0.0003\n",
      "Episode: 739, Total reward: -45.8894775821173, Average Rewards: -63.0426859058267, Epsilon: 0.09, Learning_rate: 0.0003\n",
      "Episode: 740, Total reward: -108.61466452497889, Average Rewards: -63.83305643495184, Epsilon: 0.09, Learning_rate: 0.0003\n",
      "Episode: 741, Total reward: -65.47794691434545, Average Rewards: -63.02883607511667, Epsilon: 0.08, Learning_rate: 0.0003\n",
      "Episode: 742, Total reward: -29.66694600791489, Average Rewards: -63.20168356160846, Epsilon: 0.07, Learning_rate: 0.0003\n",
      "Episode: 743, Total reward: -137.88873195060881, Average Rewards: -61.26170218121406, Epsilon: 0.07, Learning_rate: 0.0003\n",
      "Episode: 744, Total reward: 7.480795544512058, Average Rewards: -61.88374785863104, Epsilon: 0.07, Learning_rate: 0.0003\n",
      "Episode: 745, Total reward: -42.78880324803751, Average Rewards: -60.71378213597236, Epsilon: 0.06, Learning_rate: 0.0003\n",
      "Episode: 746, Total reward: -37.21421648457102, Average Rewards: -60.65950629496049, Epsilon: 0.05, Learning_rate: 0.0003\n",
      "Episode: 747, Total reward: -25.058978344933635, Average Rewards: -60.619761587865554, Epsilon: 0.05, Learning_rate: 0.0003\n",
      "Episode: 748, Total reward: -57.57634058901064, Average Rewards: -58.964463838074586, Epsilon: 0.04, Learning_rate: 0.0003\n",
      "Episode: 749, Total reward: -33.30346656048464, Average Rewards: -58.944659352297094, Epsilon: 0.03, Learning_rate: 0.0003\n",
      "Episode: 750, Total reward: -238.60249097752674, Average Rewards: -59.78370700280625, Epsilon: 0.03, Learning_rate: 0.0003\n",
      "Episode: 751, Total reward: -61.06538258732551, Average Rewards: -61.98916346042337, Epsilon: 0.02, Learning_rate: 0.0003\n",
      "Episode: 752, Total reward: -163.61891238764522, Average Rewards: -62.44380305155598, Epsilon: 0.02, Learning_rate: 0.0003\n",
      "Episode: 753, Total reward: -142.75509161506668, Average Rewards: -64.19173978742786, Epsilon: 0.02, Learning_rate: 0.0003\n",
      "Episode: 754, Total reward: -44.250646020646904, Average Rewards: -65.2297826639734, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 755, Total reward: -45.343150402381305, Average Rewards: -65.43923673987132, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 756, Total reward: -142.6499521262605, Average Rewards: -65.13059169295795, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 757, Total reward: -115.00691695099, Average Rewards: -66.2156682315756, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 758, Total reward: -133.6031788740946, Average Rewards: -67.85956067456867, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 759, Total reward: -78.53916839727691, Average Rewards: -69.05218123551623, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 760, Total reward: -37.81622818660764, Average Rewards: -68.66610341214071, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 761, Total reward: -23.124385465652278, Average Rewards: -68.47392103097467, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 762, Total reward: -236.35198176532984, Average Rewards: -68.52269749330209, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 763, Total reward: -169.59507461360334, Average Rewards: -70.64858742412645, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 764, Total reward: -180.20460918240371, Average Rewards: -72.19238404434776, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 765, Total reward: -17.813117197325163, Average Rewards: -73.10545457766233, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 766, Total reward: -205.1149812064566, Average Rewards: -72.71136555923621, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 767, Total reward: -137.42149638951545, Average Rewards: -73.74456210068745, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 768, Total reward: -69.35940230613744, Average Rewards: -74.79581150397104, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 769, Total reward: -113.42569731101025, Average Rewards: -75.78983197939223, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 770, Total reward: -65.89841092664086, Average Rewards: -75.16020186242389, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 771, Total reward: -175.63186014195242, Average Rewards: -75.13098117304439, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 772, Total reward: -38.66247627713677, Average Rewards: -76.45299827081656, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 773, Total reward: -247.94146575527606, Average Rewards: -76.05165538475724, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 774, Total reward: -25.337715815373688, Average Rewards: -76.9238393182185, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 775, Total reward: -45.41889641095201, Average Rewards: -76.31894475797623, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 776, Total reward: -2.271999251725175, Average Rewards: -75.35170687133694, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 777, Total reward: -202.4342810038895, Average Rewards: -75.20768680626497, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 778, Total reward: -36.2535118613475, Average Rewards: -75.1294570840592, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 779, Total reward: -160.54385404117698, Average Rewards: -74.88740938289017, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 780, Total reward: -10.424309764014406, Average Rewards: -75.5964464022837, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 781, Total reward: -41.742239590654705, Average Rewards: -75.23433891576047, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 782, Total reward: -23.74853351925836, Average Rewards: -75.11745521803977, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 783, Total reward: -72.43747818411316, Average Rewards: -74.68934439153844, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 784, Total reward: -147.76014677826467, Average Rewards: -74.86118205719514, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 785, Total reward: -119.6742044956787, Average Rewards: -75.857427552303, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 786, Total reward: -176.50755644002467, Average Rewards: -73.80448252159107, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 787, Total reward: -128.8242971369089, Average Rewards: -73.43190511863244, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 788, Total reward: -208.50488432986737, Average Rewards: -75.2802951843059, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 789, Total reward: -123.73015069513586, Average Rewards: -76.33496617798536, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 790, Total reward: -84.6970956719901, Average Rewards: -76.20898399468983, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 791, Total reward: -209.78404779463804, Average Rewards: -76.25117872236821, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 792, Total reward: -177.88588635248294, Average Rewards: -77.42631427794697, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 793, Total reward: -194.7208952723043, Average Rewards: -78.42551643134459, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 794, Total reward: -124.86802478956693, Average Rewards: -80.26326055581387, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 795, Total reward: -141.2507748839385, Average Rewards: -81.33492539993377, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 796, Total reward: -40.818602704199414, Average Rewards: -82.4484385046621, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 797, Total reward: -131.81280115114086, Average Rewards: -82.83574399425117, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 798, Total reward: -233.23083519340008, Average Rewards: -83.98370847976734, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 799, Total reward: -157.5602354086649, Average Rewards: -85.95323893863538, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 800, Total reward: -159.3668954841446, Average Rewards: -87.48787445831783, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 801, Total reward: -290.089268042567, Average Rewards: -88.9271335248229, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 802, Total reward: -122.07237807470851, Average Rewards: -91.46949958764904, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 803, Total reward: -10.453432827095854, Average Rewards: -92.48116289832211, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 804, Total reward: -5.619197615010012, Average Rewards: -92.49974620218596, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 805, Total reward: -178.33625716320435, Average Rewards: -92.03594634886412, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 806, Total reward: -48.52797362309303, Average Rewards: -93.13299873170769, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 807, Total reward: -28.843384399941225, Average Rewards: -93.41269235588585, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 808, Total reward: -182.38132648695182, Average Rewards: -92.84166384989666, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 809, Total reward: -34.2928698764784, Average Rewards: -94.18338222365934, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 810, Total reward: -162.3312641795, Average Rewards: -94.11272473161038, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 811, Total reward: -27.181990899220096, Average Rewards: -95.5662335822829, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 812, Total reward: -28.679073208978725, Average Rewards: -95.26334426930745, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 813, Total reward: -7.318851633506346, Average Rewards: -95.55344411682444, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 814, Total reward: -250.1440952777227, Average Rewards: -95.39950091942177, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 815, Total reward: -210.41352266719915, Average Rewards: -96.70211255183314, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 816, Total reward: -43.13026731303388, Average Rewards: -98.34216831746099, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 817, Total reward: -56.356529065167905, Average Rewards: -98.45093188939543, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 818, Total reward: -46.04671573735405, Average Rewards: -98.48780506734906, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 819, Total reward: -118.91734070255829, Average Rewards: -97.87633734558125, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 820, Total reward: -27.671210960163386, Average Rewards: -98.6670518224527, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 821, Total reward: -158.85551727445892, Average Rewards: -98.78874989596139, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 822, Total reward: -28.843403356915594, Average Rewards: -99.17836915780892, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 823, Total reward: -46.20207488200463, Average Rewards: -98.84437324584864, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 824, Total reward: -277.1218869209356, Average Rewards: -98.80470797839251, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 825, Total reward: -212.36953186367617, Average Rewards: -101.15540530907009, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 826, Total reward: -27.1946010996147, Average Rewards: -102.70246541564465, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 827, Total reward: -60.4920520731634, Average Rewards: -102.52427676733329, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 828, Total reward: -35.67337743779999, Average Rewards: -102.68430388762897, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 829, Total reward: -14.457098743813361, Average Rewards: -101.87218931400676, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 830, Total reward: -37.42420156798464, Average Rewards: -101.57147789782253, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 831, Total reward: -108.35154840200703, Average Rewards: -101.19759910064332, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 832, Total reward: -51.710306460344114, Average Rewards: -101.8999097989302, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 833, Total reward: -152.7291687642644, Average Rewards: -101.91483979900167, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 834, Total reward: -42.153434033823004, Average Rewards: -102.42621399455524, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 835, Total reward: -75.20176262187296, Average Rewards: -102.32515437777778, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 836, Total reward: 3.2787987279494875, Average Rewards: -101.11834843300059, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 837, Total reward: -62.2615167343932, Average Rewards: -100.83876323998403, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 838, Total reward: -29.65715070147775, Average Rewards: -101.25123678529799, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 839, Total reward: -3.5066645122835256, Average Rewards: -99.37631791715665, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 840, Total reward: -25.038815994862688, Average Rewards: -98.95248978645832, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 841, Total reward: -30.985680580341374, Average Rewards: -98.11673130115717, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 842, Total reward: -57.95224284879433, Average Rewards: -97.7718086378171, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 843, Total reward: -45.710531411766404, Average Rewards: -98.0546616062259, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 844, Total reward: -35.84344600087921, Average Rewards: -97.13287960083748, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 845, Total reward: -76.30560426224059, Average Rewards: -97.56612201629139, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 846, Total reward: -38.817494578449335, Average Rewards: -97.90129002643343, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 847, Total reward: -32.82196417800864, Average Rewards: -97.9173228073722, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 848, Total reward: -64.76723429292451, Average Rewards: -97.99495266570295, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 849, Total reward: -56.707603664859086, Average Rewards: -98.06686160274207, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 850, Total reward: -28.794968969127712, Average Rewards: -98.30090297378584, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 851, Total reward: -47.34519834236857, Average Rewards: -96.20282775370185, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 852, Total reward: -55.05526822332787, Average Rewards: -96.06562591125228, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 853, Total reward: -49.50646547242233, Average Rewards: -94.97998946960912, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 854, Total reward: -78.99241677264101, Average Rewards: -94.04750320818268, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 855, Total reward: -41.731572158021535, Average Rewards: -94.39492091570263, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 856, Total reward: -9.12527772556954, Average Rewards: -94.35880513325901, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 857, Total reward: -64.07733376231886, Average Rewards: -93.0235583892521, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 858, Total reward: -25.71280530279971, Average Rewards: -92.5142625573654, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 859, Total reward: -25.890088471254618, Average Rewards: -91.43535882165244, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 860, Total reward: -92.9561197786236, Average Rewards: -90.90886802239221, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 861, Total reward: -73.03211571116115, Average Rewards: -91.46026693831237, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 862, Total reward: -44.72299746582134, Average Rewards: -91.95934424076748, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 863, Total reward: -44.36782474018573, Average Rewards: -90.04305439777238, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 864, Total reward: -45.80169415398426, Average Rewards: -88.79078189903821, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 865, Total reward: -60.21833628152594, Average Rewards: -87.44675274875401, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 866, Total reward: -28.910877175525563, Average Rewards: -87.87080493959601, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 867, Total reward: -46.62178024895938, Average Rewards: -86.1087638992867, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 868, Total reward: -19.507444950241105, Average Rewards: -85.20076673788112, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 869, Total reward: -90.40173837107557, Average Rewards: -84.70224716432217, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 870, Total reward: -67.09941575571294, Average Rewards: -84.47200757492281, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 871, Total reward: -93.51528114867739, Average Rewards: -84.48401762321353, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 872, Total reward: -19.282790661826258, Average Rewards: -83.66285183328078, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 873, Total reward: -58.321696836389634, Average Rewards: -83.46905497712767, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 874, Total reward: -71.8270853380331, Average Rewards: -81.57285728793882, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 875, Total reward: -62.251105725241494, Average Rewards: -82.03775098316541, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 876, Total reward: -38.0553158797167, Average Rewards: -82.20607307630831, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 877, Total reward: -59.36040410244168, Average Rewards: -82.56390624258823, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 878, Total reward: -18.070227964665072, Average Rewards: -81.13316747357375, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 879, Total reward: -73.62041958622243, Average Rewards: -80.95133463460692, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 880, Total reward: -69.21046678533745, Average Rewards: -80.08210029005738, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 881, Total reward: -42.66594469677457, Average Rewards: -80.66996186027062, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 882, Total reward: -31.365786614977004, Average Rewards: -80.67919891133181, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 883, Total reward: -69.63414332082733, Average Rewards: -80.755371442289, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 884, Total reward: -20.07306648873201, Average Rewards: -80.72733809365613, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 885, Total reward: -56.93727748827001, Average Rewards: -79.45046729076081, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 886, Total reward: -23.898047946403455, Average Rewards: -78.82309802068673, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 887, Total reward: -76.14545816193491, Average Rewards: -77.29700293575053, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 888, Total reward: -70.41199950884129, Average Rewards: -76.77021454600077, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 889, Total reward: -24.624005887098107, Average Rewards: -75.38928569779051, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 890, Total reward: -25.378026328466365, Average Rewards: -74.39822424971013, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 891, Total reward: -48.28015669718872, Average Rewards: -73.80503355627489, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 892, Total reward: -73.09158659715362, Average Rewards: -72.18999464530039, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 893, Total reward: 2.8643076597606782, Average Rewards: -71.14205164774711, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 894, Total reward: -31.894426411991148, Average Rewards: -69.16619961842645, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 895, Total reward: -24.319612130030674, Average Rewards: -68.2364636346507, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 896, Total reward: -27.038895313969324, Average Rewards: -67.06715200711162, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 897, Total reward: -44.22488581602117, Average Rewards: -66.92935493320931, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 898, Total reward: -66.38643124200763, Average Rewards: -66.05347577985812, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 899, Total reward: -10.779635637319085, Average Rewards: -64.38503174034419, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 900, Total reward: -64.09735246506233, Average Rewards: -62.917225742630734, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 901, Total reward: -27.532092070149687, Average Rewards: -61.96453031243991, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 902, Total reward: -12.691884994012563, Average Rewards: -59.33895855271574, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 903, Total reward: -44.25141092561819, Average Rewards: -58.24515362190879, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 904, Total reward: -6.973813915618012, Average Rewards: -58.583133402894006, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 905, Total reward: -26.006041142309623, Average Rewards: -58.59667956590008, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 906, Total reward: -6.92189902489614, Average Rewards: -57.07337740569113, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 907, Total reward: -1.1729950298108323, Average Rewards: -56.65731665970917, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 908, Total reward: -45.7602536516701, Average Rewards: -56.380612766007864, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 909, Total reward: -49.902037722554134, Average Rewards: -55.01440203765504, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 910, Total reward: -28.045712158674363, Average Rewards: -55.1704937161158, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 911, Total reward: -36.87392888479524, Average Rewards: -53.82763819590755, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 912, Total reward: -17.149898953604882, Average Rewards: -53.9245575757633, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 913, Total reward: -45.07865483743487, Average Rewards: -53.809265833209565, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 914, Total reward: -34.5605905733883, Average Rewards: -54.18686386524885, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 915, Total reward: -25.188927679597953, Average Rewards: -52.031028818205506, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 916, Total reward: -27.12106592764677, Average Rewards: -50.1787828683295, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 917, Total reward: -34.66396038697128, Average Rewards: -50.018690854475615, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 918, Total reward: -60.78707896870863, Average Rewards: -49.80176516769367, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 919, Total reward: -50.71974026311132, Average Rewards: -49.9491688000072, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 920, Total reward: -83.84533583959187, Average Rewards: -49.267192795612736, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 921, Total reward: 9.309216479839666, Average Rewards: -49.82893404440701, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 922, Total reward: -10.753691935508762, Average Rewards: -48.14728670686402, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 923, Total reward: -19.0372377703555, Average Rewards: -47.96638959264996, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 924, Total reward: -30.21076810870013, Average Rewards: -47.69474122153347, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 925, Total reward: -62.37780845965956, Average Rewards: -45.225630033411115, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 926, Total reward: -71.26165810111365, Average Rewards: -43.72571279937096, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 927, Total reward: -100.24058169730544, Average Rewards: -44.16638336938594, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 928, Total reward: -9.781326691418085, Average Rewards: -44.56386866562736, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 929, Total reward: -1.4114895546867618, Average Rewards: -44.30494815816354, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 930, Total reward: -23.9651440045478, Average Rewards: -44.17449206627228, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 931, Total reward: -44.270399448247495, Average Rewards: -44.03990149063791, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 932, Total reward: -37.57075129666401, Average Rewards: -43.39909000110031, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 933, Total reward: -71.33489793476282, Average Rewards: -43.25769444946351, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 934, Total reward: -20.963228295525294, Average Rewards: -42.4437517411685, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 935, Total reward: -58.973794236177035, Average Rewards: -42.23184968378552, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 936, Total reward: -6.8091678644925615, Average Rewards: -42.06956999992856, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 937, Total reward: -25.874503131601777, Average Rewards: -42.170449665852985, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 938, Total reward: -70.56834977905325, Average Rewards: -41.806579529825065, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 939, Total reward: -10.319125296691729, Average Rewards: -42.21569152060082, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 940, Total reward: -44.23237835865684, Average Rewards: -42.283816128444904, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 941, Total reward: -23.788204541618978, Average Rewards: -42.47575175208285, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 942, Total reward: -49.59022744844953, Average Rewards: -42.40377699169562, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 943, Total reward: -20.55265652014379, Average Rewards: -42.32015683769218, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 944, Total reward: -1.585580291179173, Average Rewards: -42.06857808877596, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 945, Total reward: -38.442484829333104, Average Rewards: -41.72599943167895, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 946, Total reward: -35.028995585678544, Average Rewards: -41.347368237349876, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 947, Total reward: -52.721568877130125, Average Rewards: -41.309483247422165, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 948, Total reward: -9.287386960437413, Average Rewards: -41.508479294413384, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 949, Total reward: -3.8285475898564973, Average Rewards: -40.953680821088504, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 950, Total reward: -26.458738464083748, Average Rewards: -40.424890260338486, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 951, Total reward: -8.10617339323504, Average Rewards: -40.401527955288046, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 952, Total reward: -74.80472111109866, Average Rewards: -40.009137705796704, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 953, Total reward: 77.79186262681009, Average Rewards: -40.20663223467441, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 954, Total reward: -28.850784497162515, Average Rewards: -38.93364895368209, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 955, Total reward: 202.20546017896197, Average Rewards: -38.4322326309273, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 956, Total reward: -26.82986803205972, Average Rewards: -35.99286230755747, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 957, Total reward: 167.1049563686092, Average Rewards: -36.16990821062237, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 958, Total reward: -0.4205331088520465, Average Rewards: -33.85808530931309, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 959, Total reward: 0.17497182840997338, Average Rewards: -33.60516258737362, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 960, Total reward: 189.74193046868047, Average Rewards: -33.34451198437697, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 961, Total reward: 177.48985486202304, Average Rewards: -30.51753148190393, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 962, Total reward: 134.92661266508065, Average Rewards: -28.012311776172087, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 963, Total reward: -0.6559727286639041, Average Rewards: -26.21581567486307, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 964, Total reward: 124.42962093252372, Average Rewards: -25.778697154747853, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 965, Total reward: -39.2902375136256, Average Rewards: -24.076384003882772, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 966, Total reward: -11.46987766531898, Average Rewards: -23.867103016203767, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 967, Total reward: -14.379095891387603, Average Rewards: -23.6926930211017, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 968, Total reward: 11.298990430713717, Average Rewards: -23.37026617752598, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 969, Total reward: -17.05509605477331, Average Rewards: -23.062201823716435, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 970, Total reward: 1.5280983017067418, Average Rewards: -22.32873540055342, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 971, Total reward: -16.870333909459603, Average Rewards: -21.64246025997922, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 972, Total reward: -26.381701413059172, Average Rewards: -20.876010787587038, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 973, Total reward: -13.008935571545749, Average Rewards: -20.946999895099367, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 974, Total reward: -13.755923405485692, Average Rewards: -20.49387228245093, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 975, Total reward: -27.586425327080605, Average Rewards: -19.913160663125453, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 976, Total reward: -1.601003066364343, Average Rewards: -19.566513859143846, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 977, Total reward: 27.037148926444143, Average Rewards: -19.20197073101032, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 978, Total reward: -10.310633554755997, Average Rewards: -18.337995200721465, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 979, Total reward: 9.562344831717732, Average Rewards: -18.26039925662237, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 980, Total reward: 125.77404925582024, Average Rewards: -17.42857161244297, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 981, Total reward: 21.264127692276336, Average Rewards: -15.47872645203139, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 982, Total reward: -9.708192165939026, Average Rewards: -14.839425728140885, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 983, Total reward: 183.86989655301286, Average Rewards: -14.622849783650501, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 984, Total reward: -21.892060816093608, Average Rewards: -12.0878093849121, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 985, Total reward: 66.09296787963494, Average Rewards: -12.105999328185717, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 986, Total reward: 158.03536365597898, Average Rewards: -10.875696874506668, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 987, Total reward: 15.275507879718305, Average Rewards: -9.056362758482846, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 988, Total reward: -1.9759124281059095, Average Rewards: -8.142153098066313, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 989, Total reward: 102.7716662427247, Average Rewards: -7.4577922272589605, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 990, Total reward: -2.2761842574753794, Average Rewards: -6.18383550596073, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 991, Total reward: 5.05584281331299, Average Rewards: -5.952817085250823, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 992, Total reward: -16.591792633830924, Average Rewards: -5.419457090145804, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 993, Total reward: -32.820914970769046, Average Rewards: -4.854459150512579, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 994, Total reward: -14.530598057311646, Average Rewards: -5.211311376817876, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 995, Total reward: 151.51952719769866, Average Rewards: -5.0376730932710805, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 996, Total reward: -8.237854598730198, Average Rewards: -3.279281699993786, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 997, Total reward: -26.13274596403844, Average Rewards: -3.0912712928413955, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 998, Total reward: 18.110147142626758, Average Rewards: -2.9103498943215684, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 999, Total reward: 5.494370293125145, Average Rewards: -2.0653841104752244, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1000, Total reward: 2.4852862183416695, Average Rewards: -1.9026440511707825, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1001, Total reward: -7.724832209823588, Average Rewards: -1.2368176643367423, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1002, Total reward: 4.99989247753121, Average Rewards: -1.0387450657334802, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1003, Total reward: -7.720380539556878, Average Rewards: -0.8618272910180431, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1004, Total reward: -15.951176512258476, Average Rewards: -0.49651698715743026, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1005, Total reward: -2.0346022554199568, Average Rewards: -0.5862906131238343, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1006, Total reward: -2.3276754291494655, Average Rewards: -0.3465762242549376, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1007, Total reward: 4.531757824774598, Average Rewards: -0.300633988297471, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1008, Total reward: 235.1514765671418, Average Rewards: -0.24358645975161589, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1009, Total reward: 20.583431433080644, Average Rewards: 2.565530842436502, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1010, Total reward: -40.637582869839186, Average Rewards: 3.2703855339928505, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1011, Total reward: 213.17609629206504, Average Rewards: 3.1444668268812017, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1012, Total reward: -30.381557447399267, Average Rewards: 5.644967078649804, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1013, Total reward: -30.68256234400159, Average Rewards: 5.5126504937118606, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1014, Total reward: 22.722193482810162, Average Rewards: 5.656611418646194, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1015, Total reward: 17.445165645428002, Average Rewards: 6.22943925920818, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1016, Total reward: 166.42626198898753, Average Rewards: 6.6557801924584385, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1017, Total reward: 12.893940035169699, Average Rewards: 8.59125347162478, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1018, Total reward: 1.5296983051754167, Average Rewards: 9.066832475846192, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1019, Total reward: 14.032292758408627, Average Rewards: 9.690000248585031, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1020, Total reward: 0.21925079911625112, Average Rewards: 10.33752057880023, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1021, Total reward: -11.244517364045873, Average Rewards: 11.17816644518731, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1022, Total reward: -8.735083509734318, Average Rewards: 10.972629106748455, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1023, Total reward: 64.15838116597097, Average Rewards: 10.9928151910062, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1024, Total reward: 21.95172362198899, Average Rewards: 11.824771380369464, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1025, Total reward: -10.822399728938434, Average Rewards: 12.346396297676357, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1026, Total reward: -36.41089258149078, Average Rewards: 12.861950384983565, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1027, Total reward: 196.49577772811864, Average Rewards: 13.210458040179795, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1028, Total reward: -43.511986306562555, Average Rewards: 16.177821634434036, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1029, Total reward: 21.86343911965386, Average Rewards: 15.840515038282593, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1030, Total reward: 211.7698257595289, Average Rewards: 16.073264325026, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1031, Total reward: -10.097025495111245, Average Rewards: 18.430614022666767, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1032, Total reward: 163.8350823925571, Average Rewards: 18.772347762198127, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1033, Total reward: -0.5315524125542981, Average Rewards: 20.786406099090335, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1034, Total reward: -29.82332862228865, Average Rewards: 21.49443955431242, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1035, Total reward: -21.013581873463536, Average Rewards: 21.40583855104479, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1036, Total reward: 172.51534061498694, Average Rewards: 21.78544067467193, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1037, Total reward: 7.266475211334541, Average Rewards: 23.578685759466723, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1038, Total reward: 10.383483535893951, Average Rewards: 23.910095542896087, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1039, Total reward: 22.528310694745667, Average Rewards: 24.71961387604556, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1040, Total reward: 11.51291725717761, Average Rewards: 25.048088235959934, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1041, Total reward: -2.6924305980660725, Average Rewards: 25.60554119211828, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1042, Total reward: -31.94705547097917, Average Rewards: 25.81649893155381, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1043, Total reward: 7.724928903399975, Average Rewards: 25.992930651328514, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1044, Total reward: -22.185642840344492, Average Rewards: 26.27570650556395, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1045, Total reward: -6.143465073446259, Average Rewards: 26.0697058800723, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1046, Total reward: 136.5024859867445, Average Rewards: 26.39269607763116, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1047, Total reward: -84.855232758588, Average Rewards: 28.108010893355395, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1048, Total reward: 139.10176797604782, Average Rewards: 27.786674254540813, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1049, Total reward: 18.768073322853656, Average Rewards: 29.270565803905665, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1050, Total reward: 18.394485898207243, Average Rewards: 29.496532013032766, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1051, Total reward: 111.6408377299673, Average Rewards: 29.945064256655677, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1052, Total reward: 146.46433464037676, Average Rewards: 31.1425343678877, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1053, Total reward: 166.23355584757348, Average Rewards: 33.355224925402446, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1054, Total reward: 20.960907502873628, Average Rewards: 34.23964185761009, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1055, Total reward: 179.0029845635246, Average Rewards: 34.73775877761045, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1056, Total reward: 11.86692397077079, Average Rewards: 34.50573402145608, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1057, Total reward: 149.48018856118654, Average Rewards: 34.892701941484376, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1058, Total reward: -60.789182698603156, Average Rewards: 34.71645426341015, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1059, Total reward: -36.0634326212061, Average Rewards: 34.112767767512636, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1060, Total reward: 40.314055723654704, Average Rewards: 33.750383723016476, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1061, Total reward: 1.087433797221126, Average Rewards: 32.256104975566224, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1062, Total reward: -7.9638586710286665, Average Rewards: 30.4920807649182, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1063, Total reward: 39.7853487396939, Average Rewards: 29.063176051557114, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1064, Total reward: 7.3992224115125165, Average Rewards: 29.46758926624069, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1065, Total reward: 167.1610114370857, Average Rewards: 28.297285281030582, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1066, Total reward: 2.539403875971221, Average Rewards: 30.36179777053769, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1067, Total reward: -5.007283045834966, Average Rewards: 30.501890585950594, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1068, Total reward: 170.53778711873468, Average Rewards: 30.595608714406122, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1069, Total reward: 215.90420913823127, Average Rewards: 32.18799668128633, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1070, Total reward: 161.4269136514087, Average Rewards: 34.517589733216376, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1071, Total reward: 5.534453118897679, Average Rewards: 36.116577886713394, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1072, Total reward: 172.93005536595425, Average Rewards: 36.340625756996964, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1073, Total reward: 161.62293091191117, Average Rewards: 38.33374332478709, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1074, Total reward: 192.3897042521939, Average Rewards: 40.08006198962167, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1075, Total reward: -0.7774055935676798, Average Rewards: 42.14151826619846, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1076, Total reward: 128.3635970620985, Average Rewards: 42.409608463533594, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1077, Total reward: 12.458260033676433, Average Rewards: 43.70925446481822, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1078, Total reward: 194.44898635751628, Average Rewards: 43.56346557589053, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1079, Total reward: 151.87114266461083, Average Rewards: 45.61106177501326, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1080, Total reward: 149.6864123640671, Average Rewards: 47.0341497533422, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1081, Total reward: -34.14499422476236, Average Rewards: 47.27327338442466, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1082, Total reward: 164.40037454219046, Average Rewards: 46.71918216525427, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1083, Total reward: 148.37733339876837, Average Rewards: 48.46026783233557, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1084, Total reward: 206.70371304772556, Average Rewards: 48.10534220079313, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1085, Total reward: 175.17728682304906, Average Rewards: 50.39129993943132, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1086, Total reward: -28.60391783177814, Average Rewards: 51.48214312886547, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1087, Total reward: 204.39851905251473, Average Rewards: 49.61575031398789, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1088, Total reward: 188.97708154623132, Average Rewards: 51.50698042571587, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1089, Total reward: 204.81924731186965, Average Rewards: 53.41651036545923, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1090, Total reward: 194.19731606946695, Average Rewards: 54.43698617615068, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1091, Total reward: 188.96137685400018, Average Rewards: 56.40172117942011, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1092, Total reward: 196.8188849208566, Average Rewards: 58.24077651982699, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1093, Total reward: 224.71837642658738, Average Rewards: 60.374883295373856, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1094, Total reward: 205.84193302055183, Average Rewards: 62.95027620934742, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1095, Total reward: 215.96718269506323, Average Rewards: 65.15400152012604, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1096, Total reward: 205.26727494535226, Average Rewards: 65.7984780750997, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1097, Total reward: 145.21168329938484, Average Rewards: 67.93352937054053, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1098, Total reward: 173.91125501517172, Average Rewards: 69.64697366317476, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1099, Total reward: 167.5223374344494, Average Rewards: 71.2049847419002, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1100, Total reward: 224.42054479685441, Average Rewards: 72.82526441331345, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1101, Total reward: 205.5966175075642, Average Rewards: 75.04461699909858, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1102, Total reward: 156.46375767214633, Average Rewards: 77.17783149627245, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1103, Total reward: 243.11719686288183, Average Rewards: 78.6924701482186, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1104, Total reward: 185.95620394265052, Average Rewards: 81.20084592224299, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1105, Total reward: 236.02601026621178, Average Rewards: 83.21991972679209, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1106, Total reward: 209.79629023382958, Average Rewards: 85.60052585200839, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1107, Total reward: 232.95194102930955, Average Rewards: 87.72176550863819, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1108, Total reward: 235.64449352696215, Average Rewards: 90.00596734068354, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1109, Total reward: 178.96165140505428, Average Rewards: 90.01089751028174, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1110, Total reward: 261.8294572682613, Average Rewards: 91.59467971000147, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1111, Total reward: 246.21236574954114, Average Rewards: 94.61935011138246, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1112, Total reward: 236.94297373739587, Average Rewards: 94.94971280595725, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1113, Total reward: 148.4082538492213, Average Rewards: 97.62295811780517, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1114, Total reward: 246.28543801660024, Average Rewards: 99.41386627973743, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1115, Total reward: 198.64371723696163, Average Rewards: 101.64949872507533, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1116, Total reward: 217.5632447074728, Average Rewards: 103.46148424099069, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1117, Total reward: 268.70714688107785, Average Rewards: 103.97285406817554, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1118, Total reward: 247.6676027252934, Average Rewards: 106.53098613663462, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1119, Total reward: 182.12134788074354, Average Rewards: 108.9923651808358, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1120, Total reward: 236.3956965393622, Average Rewards: 110.67325573205915, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1121, Total reward: 206.83809609853455, Average Rewards: 113.03502018946159, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1122, Total reward: 230.297237313778, Average Rewards: 115.2158463240874, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1123, Total reward: -22.056299094826574, Average Rewards: 117.60616953232253, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1124, Total reward: 249.76527767730565, Average Rewards: 116.74402272971453, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1125, Total reward: 172.0512740930352, Average Rewards: 119.02215827026768, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1126, Total reward: 237.98161153265792, Average Rewards: 120.85089500848744, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1127, Total reward: -14.971395180690065, Average Rewards: 123.59482004962891, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1128, Total reward: 240.11941279994375, Average Rewards: 121.48014832054083, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1129, Total reward: 234.89911425892487, Average Rewards: 124.3164623116059, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1130, Total reward: 202.92312721752864, Average Rewards: 126.44681906299861, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1131, Total reward: 209.08366671917713, Average Rewards: 126.35835207757864, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1132, Total reward: 261.8617774475164, Average Rewards: 128.55015899972153, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1133, Total reward: 278.6410816207098, Average Rewards: 129.5304259502711, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1134, Total reward: 197.4739362082122, Average Rewards: 132.32215229060373, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1135, Total reward: 199.41513922217263, Average Rewards: 134.59512493890875, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1136, Total reward: 180.12004378330184, Average Rewards: 136.7994121498651, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1137, Total reward: -479.69644948237624, Average Rewards: 136.87545918154825, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1138, Total reward: 221.00824084301044, Average Rewards: 132.00582993461114, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1139, Total reward: 242.16884183156628, Average Rewards: 134.11207750768227, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1140, Total reward: 242.71875020116985, Average Rewards: 136.30848281905048, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1141, Total reward: 224.77106857726864, Average Rewards: 138.62054114849042, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1142, Total reward: 215.13897513485028, Average Rewards: 140.89517614024376, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1143, Total reward: 230.49471627943703, Average Rewards: 143.36603644630208, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1144, Total reward: 252.01913069818656, Average Rewards: 145.59373432006248, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1145, Total reward: -443.99169460067446, Average Rewards: 148.3357820554478, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1146, Total reward: 231.38452149844153, Average Rewards: 143.9572997601755, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1147, Total reward: 237.9501706154163, Average Rewards: 144.90612011529245, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1148, Total reward: 195.20867386296064, Average Rewards: 148.1341741490325, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1149, Total reward: 246.56269785163738, Average Rewards: 148.69524320790163, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1150, Total reward: 180.109369268369, Average Rewards: 150.97318945318943, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1151, Total reward: 233.63827177946249, Average Rewards: 152.59033828689107, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1152, Total reward: 279.4657091099052, Average Rewards: 153.810312627386, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1153, Total reward: 254.97501735004445, Average Rewards: 155.14032637208132, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1154, Total reward: 220.5083032006514, Average Rewards: 156.027740987106, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1155, Total reward: 289.4904595164878, Average Rewards: 158.0232149440838, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1156, Total reward: 271.0882046636536, Average Rewards: 159.12808969361342, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1157, Total reward: 183.05935404993545, Average Rewards: 161.7203025005423, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1158, Total reward: 208.4876299336693, Average Rewards: 162.05609415542975, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1159, Total reward: 241.46001280385374, Average Rewards: 164.74886228175248, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1160, Total reward: 260.66176360874545, Average Rewards: 167.52409673600306, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1161, Total reward: 200.48584180926468, Average Rewards: 169.727573814854, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1162, Total reward: 200.9897742780737, Average Rewards: 171.72155789497444, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1163, Total reward: 244.1655063418071, Average Rewards: 173.81109422446545, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1164, Total reward: 273.1285848375346, Average Rewards: 175.8548958004866, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1165, Total reward: 204.47720795639634, Average Rewards: 178.5121894247468, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1166, Total reward: 262.9984526024193, Average Rewards: 178.88535138993987, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1167, Total reward: -43.06338707973256, Average Rewards: 181.48994187720436, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1168, Total reward: 214.99302690924682, Average Rewards: 181.1093808368654, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1169, Total reward: 217.92449871180156, Average Rewards: 181.5539332347705, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1170, Total reward: 211.57847194682185, Average Rewards: 181.5741361305062, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1171, Total reward: 250.55461690901774, Average Rewards: 182.07565171346033, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1172, Total reward: 222.9022039168829, Average Rewards: 184.52585335136155, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1173, Total reward: 228.18348205953365, Average Rewards: 185.0255748368708, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1174, Total reward: 232.6304608233533, Average Rewards: 185.69118034834705, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1175, Total reward: 221.62878563360596, Average Rewards: 186.09358791405862, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1176, Total reward: 212.38973452785834, Average Rewards: 188.31764982633035, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1177, Total reward: 148.13612945135736, Average Rewards: 189.157911200988, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1178, Total reward: 183.11718777159928, Average Rewards: 190.51468989516485, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1179, Total reward: 250.21852684857203, Average Rewards: 190.40137190930568, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1180, Total reward: 259.21729630229265, Average Rewards: 191.3848457511453, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1181, Total reward: 5.406655726935635, Average Rewards: 192.48015459052752, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1182, Total reward: 260.308815168666, Average Rewards: 192.87567109004448, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1183, Total reward: 223.3428046165733, Average Rewards: 193.8347554963092, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1184, Total reward: 256.90815379010644, Average Rewards: 194.58441020848727, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1185, Total reward: 241.3433772440938, Average Rewards: 195.08645461591107, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1186, Total reward: 197.33861383605128, Average Rewards: 195.7481155201215, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1187, Total reward: 171.49828374894082, Average Rewards: 198.00754083679985, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1188, Total reward: 249.64450788254487, Average Rewards: 197.67853848376413, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1189, Total reward: 250.24413011734654, Average Rewards: 198.28521274712728, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1190, Total reward: 215.6041865036575, Average Rewards: 198.739461575182, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1191, Total reward: 299.50001376121565, Average Rewards: 198.95353027952387, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1192, Total reward: 218.09878204466133, Average Rewards: 200.05891664859607, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1193, Total reward: 269.76286866571616, Average Rewards: 200.2717156198341, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1194, Total reward: -75.81279949946958, Average Rewards: 200.7221605422254, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1195, Total reward: 202.7346400563947, Average Rewards: 197.90561321702518, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1196, Total reward: 218.28245905622495, Average Rewards: 197.77328779063845, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1197, Total reward: 274.9510600428424, Average Rewards: 197.9034396317472, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1198, Total reward: 267.72793776119045, Average Rewards: 199.2008333991818, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1199, Total reward: 255.51828383283777, Average Rewards: 200.139000226642, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1200, Total reward: 276.2244565959685, Average Rewards: 201.01895969062585, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1201, Total reward: 218.15553997774973, Average Rewards: 201.53699880861703, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1202, Total reward: 211.6423415618167, Average Rewards: 201.66258803331885, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1203, Total reward: 304.4470323263794, Average Rewards: 202.21437387221556, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1204, Total reward: -183.26808055525964, Average Rewards: 202.82767222685052, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1205, Total reward: 282.0054288090809, Average Rewards: 199.1354293818714, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1206, Total reward: 245.92432482526726, Average Rewards: 199.59522356730008, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1207, Total reward: 280.6900724508943, Average Rewards: 199.95650391321448, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1208, Total reward: 226.55815657568274, Average Rewards: 200.43388522743035, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1209, Total reward: 241.38544298534126, Average Rewards: 200.34302185791756, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1210, Total reward: 228.42809961043804, Average Rewards: 200.96725977372046, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1211, Total reward: -181.61275104116834, Average Rewards: 200.63324619714226, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1212, Total reward: 212.57774340695636, Average Rewards: 196.35499502923514, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1213, Total reward: 250.64806551038788, Average Rewards: 196.11134272593074, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1214, Total reward: 157.81886984240754, Average Rewards: 197.13374084254238, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1215, Total reward: 270.1182626129824, Average Rewards: 196.2490751608005, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1216, Total reward: -71.70434508676412, Average Rewards: 196.9638206145607, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1217, Total reward: 221.6206806083357, Average Rewards: 194.07114471661828, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1218, Total reward: 246.19328871278674, Average Rewards: 193.60028005389086, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1219, Total reward: 243.33388193446666, Average Rewards: 193.5855369137658, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1220, Total reward: 255.90010130190873, Average Rewards: 194.19766225430305, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1221, Total reward: 218.43565409316523, Average Rewards: 194.39270630192848, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1222, Total reward: 259.066223328517, Average Rewards: 194.50868188187485, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1223, Total reward: 243.86430266832767, Average Rewards: 194.79637174202225, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1224, Total reward: 226.23112159548936, Average Rewards: 197.45557775965375, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1225, Total reward: 239.80663540906315, Average Rewards: 197.22023619883558, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1226, Total reward: 269.41125761741614, Average Rewards: 197.8977898119959, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1227, Total reward: 230.16065950502917, Average Rewards: 198.21208627284344, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1228, Total reward: 263.20865524539835, Average Rewards: 200.6634068197006, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1229, Total reward: 247.98236311986693, Average Rewards: 200.89429924415515, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1230, Total reward: 252.4817117781984, Average Rewards: 201.0251317327646, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1231, Total reward: 227.93881306056534, Average Rewards: 201.52071757837126, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1232, Total reward: 247.78811272438634, Average Rewards: 201.70926904178515, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1233, Total reward: 239.23038480771518, Average Rewards: 201.56853239455384, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1234, Total reward: 231.23987620094195, Average Rewards: 201.17442542642388, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1235, Total reward: 249.9610287238515, Average Rewards: 201.5120848263512, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1236, Total reward: 210.70204073996558, Average Rewards: 202.01754372136799, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1237, Total reward: 234.9518889281066, Average Rewards: 202.3233636909346, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1238, Total reward: 222.96436597834207, Average Rewards: 209.46984707503944, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1239, Total reward: 276.17879362364965, Average Rewards: 209.4894083263928, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1240, Total reward: 240.45731582934494, Average Rewards: 209.82950784431364, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1241, Total reward: 256.25656023651305, Average Rewards: 209.8068935005954, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1242, Total reward: 240.3937036600678, Average Rewards: 210.12174841718786, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1243, Total reward: 227.12204555006144, Average Rewards: 210.37429570244004, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1244, Total reward: 213.31354362229712, Average Rewards: 210.34056899514633, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1245, Total reward: 83.41802445843777, Average Rewards: 209.9535131243874, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1246, Total reward: 270.5775571004497, Average Rewards: 215.22761031497856, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1247, Total reward: 235.58429461184215, Average Rewards: 215.6195406709986, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1248, Total reward: 237.99570694741064, Average Rewards: 215.59588191096285, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1249, Total reward: -3.9553554386687435, Average Rewards: 216.02375224180733, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1250, Total reward: 226.50302354600933, Average Rewards: 213.51857170890423, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1251, Total reward: 270.18595138479213, Average Rewards: 213.98250825168066, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1252, Total reward: 280.57205496739965, Average Rewards: 214.34798504773397, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1253, Total reward: 273.2599330015568, Average Rewards: 214.3590485063089, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1254, Total reward: 181.96676596646637, Average Rewards: 214.54189766282406, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1255, Total reward: 274.26855781124704, Average Rewards: 214.1564822904822, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1256, Total reward: 248.64011808746335, Average Rewards: 214.00426327342979, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1257, Total reward: -164.77567382268438, Average Rewards: 213.77978240766785, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1258, Total reward: 223.32972203432217, Average Rewards: 210.30143212894166, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1259, Total reward: 281.52100452428874, Average Rewards: 210.4498530499482, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1260, Total reward: 253.05566480473027, Average Rewards: 210.85046296715254, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1261, Total reward: 205.46545740648693, Average Rewards: 210.7744019791124, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1262, Total reward: 247.86583054803896, Average Rewards: 210.82419813508463, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1263, Total reward: 235.41128316141953, Average Rewards: 211.29295869778426, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1264, Total reward: 185.46174060669512, Average Rewards: 211.20541646598042, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1265, Total reward: 234.03332516834087, Average Rewards: 210.32874802367198, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1266, Total reward: 231.48295406467793, Average Rewards: 210.62430919579143, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1267, Total reward: 234.70980849164874, Average Rewards: 210.30915421041402, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1268, Total reward: 267.34997306623217, Average Rewards: 213.08688616612784, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1269, Total reward: 287.54818608539017, Average Rewards: 213.6104556276977, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1270, Total reward: 223.2634903723448, Average Rewards: 214.30669250143362, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1271, Total reward: 225.19839352636768, Average Rewards: 214.4235426856888, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1272, Total reward: 225.74982014928045, Average Rewards: 214.16998045186227, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1273, Total reward: 235.68986420361847, Average Rewards: 214.19845661418628, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1274, Total reward: 245.88013552366297, Average Rewards: 214.27352043562715, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1275, Total reward: 265.8925661643375, Average Rewards: 214.40601718263025, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1276, Total reward: 212.44556805721027, Average Rewards: 214.84865498793755, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1277, Total reward: 260.25004129803517, Average Rewards: 214.84921332323106, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1278, Total reward: 223.18337794641687, Average Rewards: 215.97035244169783, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1279, Total reward: 236.47943077801756, Average Rewards: 216.371014343446, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1280, Total reward: 248.92551323399962, Average Rewards: 216.2336233827405, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1281, Total reward: 247.5454076665571, Average Rewards: 216.13070555205752, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1282, Total reward: 289.6119678217633, Average Rewards: 218.55209307145378, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1283, Total reward: 254.35776471774616, Average Rewards: 218.8451245979847, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1284, Total reward: 173.5819578544057, Average Rewards: 219.1552741989964, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1285, Total reward: 209.69995320449684, Average Rewards: 218.3220122396394, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1286, Total reward: 279.1745696943972, Average Rewards: 218.0055779992435, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1287, Total reward: 253.24407719220494, Average Rewards: 218.82393755782695, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1288, Total reward: 277.22723476199144, Average Rewards: 219.64139549225962, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1289, Total reward: 285.16523025203577, Average Rewards: 219.91722276105406, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1290, Total reward: 274.1085340051362, Average Rewards: 220.2664337624009, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1291, Total reward: 284.7058643036104, Average Rewards: 220.85147723741568, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1292, Total reward: 240.79816376947252, Average Rewards: 220.70353574283962, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1293, Total reward: 229.65450547340046, Average Rewards: 220.9305295600878, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1294, Total reward: 288.49063234432367, Average Rewards: 220.52944592816465, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1295, Total reward: 271.9596890305904, Average Rewards: 224.17248024660256, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1296, Total reward: 281.2290066220288, Average Rewards: 224.86473073634454, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1297, Total reward: 251.8224780992451, Average Rewards: 225.4941962120026, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1298, Total reward: 248.2565195468805, Average Rewards: 225.2629103925666, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1299, Total reward: 249.15759674395403, Average Rewards: 225.0681962104235, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1300, Total reward: 239.1955581541676, Average Rewards: 225.00458933953465, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1301, Total reward: 235.59630387372226, Average Rewards: 224.6343003551166, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1302, Total reward: 255.76183571931475, Average Rewards: 224.80870799407631, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1303, Total reward: 250.03053465814463, Average Rewards: 225.2499029356513, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1304, Total reward: 292.01266066856215, Average Rewards: 224.70573795896897, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1305, Total reward: 271.0672262095402, Average Rewards: 229.45854537120718, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1306, Total reward: 276.60467483988384, Average Rewards: 229.3491633452118, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1307, Total reward: 249.3323995672611, Average Rewards: 229.65596684535797, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1308, Total reward: 220.20706361362957, Average Rewards: 229.34239011652164, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1309, Total reward: 256.3890647348727, Average Rewards: 229.2788791869011, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1310, Total reward: 245.33148615328977, Average Rewards: 229.42891540439643, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1311, Total reward: 266.99526082984175, Average Rewards: 229.59794926982497, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1312, Total reward: 67.40772597929602, Average Rewards: 234.08402938853504, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1313, Total reward: 242.22366876198635, Average Rewards: 232.63232921425845, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1314, Total reward: 287.5470265213886, Average Rewards: 232.54808524677443, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1315, Total reward: 279.247376117935, Average Rewards: 233.84536681356425, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1316, Total reward: 262.03594009057997, Average Rewards: 233.9366579486138, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1317, Total reward: 249.06303988069106, Average Rewards: 237.2740608003872, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1318, Total reward: 294.4909475252162, Average Rewards: 237.54848439311073, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1319, Total reward: 276.6194499050688, Average Rewards: 238.03146098123503, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1320, Total reward: 245.56651258827844, Average Rewards: 238.36431666094109, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1321, Total reward: 306.51400313604506, Average Rewards: 238.26098077380473, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1322, Total reward: 306.2854555918516, Average Rewards: 239.14176426423353, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1323, Total reward: 265.6900219241303, Average Rewards: 239.61395658686692, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1324, Total reward: 255.92523081227517, Average Rewards: 239.83221377942493, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1325, Total reward: 265.52468728485644, Average Rewards: 240.1291548715928, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1326, Total reward: 277.06452902773935, Average Rewards: 240.3863353903507, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1327, Total reward: 144.40193047887283, Average Rewards: 240.46286810445392, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1328, Total reward: 277.352545005582, Average Rewards: 239.60528081419233, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1329, Total reward: 239.19008100880862, Average Rewards: 239.74671971179424, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1330, Total reward: 265.88084494623206, Average Rewards: 239.65879689068362, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1331, Total reward: 230.932643795579, Average Rewards: 239.79278822236395, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1332, Total reward: 291.8949584435684, Average Rewards: 239.8227265297141, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1333, Total reward: 224.44200448133648, Average Rewards: 240.26379498690594, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1334, Total reward: -89.13793109875301, Average Rewards: 240.11591118364214, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1335, Total reward: 247.69480165170188, Average Rewards: 236.9121331106452, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1336, Total reward: 268.62847603323627, Average Rewards: 236.88947083992372, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1337, Total reward: 262.7939061340493, Average Rewards: 237.46873519285637, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1338, Total reward: 277.25822196854443, Average Rewards: 237.74715536491578, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1339, Total reward: 258.6635757620171, Average Rewards: 238.29009392481782, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1340, Total reward: 240.55453085492533, Average Rewards: 238.11494174620148, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1341, Total reward: 283.0182620263578, Average Rewards: 238.1159138964573, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1342, Total reward: 255.2703832617602, Average Rewards: 238.3835309143558, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1343, Total reward: 255.93111509934627, Average Rewards: 238.53229771037275, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1344, Total reward: 240.8330161471191, Average Rewards: 238.82038840586557, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1345, Total reward: 250.72628894341028, Average Rewards: 239.09558313111378, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1346, Total reward: 263.2912924475484, Average Rewards: 240.76866577596348, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1347, Total reward: 261.1206162616911, Average Rewards: 240.6958031294345, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1348, Total reward: 264.66922775354203, Average Rewards: 240.95116634593302, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1349, Total reward: 261.2336803990273, Average Rewards: 241.21790155399427, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1350, Total reward: 241.46263120791775, Average Rewards: 243.86979191237123, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1351, Total reward: 263.2448341412646, Average Rewards: 244.01938798899027, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1352, Total reward: 276.8839776718935, Average Rewards: 243.949976816555, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1353, Total reward: 248.42349824353488, Average Rewards: 243.91309604359998, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1354, Total reward: 267.4316380667889, Average Rewards: 243.66473169601974, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1355, Total reward: 250.80795325844846, Average Rewards: 244.51938041702294, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1356, Total reward: 208.66020374394998, Average Rewards: 244.28477437149502, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1357, Total reward: 236.52447222753904, Average Rewards: 243.8849752280599, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1358, Total reward: 279.30416757840237, Average Rewards: 247.89797668856212, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1359, Total reward: 258.00080805878656, Average Rewards: 248.45772114400293, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1360, Total reward: 253.35159080482362, Average Rewards: 248.2225191793479, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1361, Total reward: 234.75862633062187, Average Rewards: 248.22547843934882, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1362, Total reward: 243.8346913536042, Average Rewards: 248.51841012859018, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1363, Total reward: 274.5722627404817, Average Rewards: 248.47809873664585, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1364, Total reward: 250.65578348524562, Average Rewards: 248.86970853243648, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1365, Total reward: 298.6472118135757, Average Rewards: 249.521648961222, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1366, Total reward: 263.28075279648056, Average Rewards: 250.16778782767432, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1367, Total reward: 271.18441400288816, Average Rewards: 250.48576581499236, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1368, Total reward: 283.0577801858175, Average Rewards: 250.8505118701047, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1369, Total reward: -27.841715155125552, Average Rewards: 251.00758994130058, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1370, Total reward: -1.8528044635503278, Average Rewards: 247.8536909288954, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1371, Total reward: 268.0478805394095, Average Rewards: 245.60252798053648, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1372, Total reward: 266.41636115512676, Average Rewards: 246.03102285066686, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1373, Total reward: 270.08476010183006, Average Rewards: 246.43768826072537, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1374, Total reward: 274.70279265367617, Average Rewards: 246.78163721970748, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1375, Total reward: 6.3464303824799515, Average Rewards: 247.0698637910076, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1376, Total reward: 230.3285819178219, Average Rewards: 244.474402433189, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1377, Total reward: 218.65965881304518, Average Rewards: 244.65323257179517, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1378, Total reward: 279.74974500287556, Average Rewards: 244.23732874694528, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1379, Total reward: 260.9634847653981, Average Rewards: 244.80299241750984, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1380, Total reward: 246.02083974099872, Average Rewards: 245.04783295738366, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1381, Total reward: 259.01115924615294, Average Rewards: 245.0187862224536, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1382, Total reward: 239.79061393394392, Average Rewards: 245.13344373824964, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1383, Total reward: 241.70250531201384, Average Rewards: 244.6352301993714, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1384, Total reward: 230.20911889011472, Average Rewards: 244.50867760531403, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1385, Total reward: 259.5167108392579, Average Rewards: 245.07494921567113, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1386, Total reward: 252.85440177517333, Average Rewards: 245.57311679201874, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1387, Total reward: 295.47221516854364, Average Rewards: 245.30991511282645, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1388, Total reward: 266.61014254211364, Average Rewards: 245.73219649258988, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1389, Total reward: 272.65834117362766, Average Rewards: 245.6260255703911, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1390, Total reward: 274.2528454205223, Average Rewards: 245.50095667960707, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1391, Total reward: 223.03247961200958, Average Rewards: 245.50239979376093, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1392, Total reward: 254.4899363953663, Average Rewards: 244.88566594684497, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1393, Total reward: 238.08199758967837, Average Rewards: 245.0225836731039, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1394, Total reward: 280.4319016768282, Average Rewards: 245.10685859426667, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1395, Total reward: -31.520626061821417, Average Rewards: 245.0262712875917, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1396, Total reward: 254.43503659815272, Average Rewards: 241.99146813666758, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1397, Total reward: 269.69272391232073, Average Rewards: 241.72352843642878, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1398, Total reward: 258.53846425866226, Average Rewards: 241.90223089455958, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1399, Total reward: 280.12016046906274, Average Rewards: 242.0050503416774, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1400, Total reward: 261.16981822560956, Average Rewards: 242.31467597892845, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1401, Total reward: 256.39690314566644, Average Rewards: 242.53441857964287, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1402, Total reward: 236.01081775803922, Average Rewards: 242.7424245723623, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1403, Total reward: 274.53986769109173, Average Rewards: 242.54491439274955, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1404, Total reward: 254.59150931285996, Average Rewards: 242.79000772307904, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1405, Total reward: 257.2785155257683, Average Rewards: 242.415796209522, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1406, Total reward: 288.55229282765697, Average Rewards: 242.27790910268436, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1407, Total reward: 281.0920821921151, Average Rewards: 242.39738528256206, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1408, Total reward: 252.43538111834636, Average Rewards: 242.71498210881057, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1409, Total reward: 253.7622284333824, Average Rewards: 243.03726528385775, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1410, Total reward: 273.8634796074736, Average Rewards: 243.01099692084284, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1411, Total reward: 269.5716719775187, Average Rewards: 243.29631685538462, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1412, Total reward: 261.78319994858794, Average Rewards: 243.32208096686142, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1413, Total reward: 236.63893500842968, Average Rewards: 245.26583570655436, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1414, Total reward: 264.5966994411254, Average Rewards: 245.20998836901876, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1415, Total reward: 263.78896182096224, Average Rewards: 244.98048509821612, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1416, Total reward: -246.1097708475321, Average Rewards: 244.8259009552464, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1417, Total reward: 272.2939447703175, Average Rewards: 239.74444384586528, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1418, Total reward: 237.86572598783337, Average Rewards: 239.97675289476155, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1419, Total reward: 233.721018614303, Average Rewards: 239.41050067938778, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1420, Total reward: 278.0792617061679, Average Rewards: 238.9815163664801, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1421, Total reward: 250.59010441616377, Average Rewards: 239.306643857659, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1422, Total reward: 257.85865263630853, Average Rewards: 238.74740487046017, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1423, Total reward: 275.70679893427405, Average Rewards: 238.26313684090474, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1424, Total reward: -38.60615352571002, Average Rewards: 238.36330461100616, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1425, Total reward: 251.4517374395433, Average Rewards: 235.41799076762632, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1426, Total reward: 239.5154843681304, Average Rewards: 235.27726126917318, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1427, Total reward: 258.19086936589343, Average Rewards: 234.90177082257708, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1428, Total reward: 258.47268048476496, Average Rewards: 236.03966021144737, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1429, Total reward: 242.12848111544346, Average Rewards: 235.8508615662392, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1430, Total reward: 235.49435052370646, Average Rewards: 235.8802455673055, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1431, Total reward: 275.71678953262335, Average Rewards: 235.57638062308027, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1432, Total reward: 289.4094749294688, Average Rewards: 236.0242220804507, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1433, Total reward: 292.2749477433953, Average Rewards: 235.99936724530968, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1434, Total reward: 244.24508495277368, Average Rewards: 236.67769667793024, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1435, Total reward: 255.61165202135464, Average Rewards: 240.0115268384455, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1436, Total reward: 290.77483376514647, Average Rewards: 240.09069534214203, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1437, Total reward: 237.35727968744573, Average Rewards: 240.31215891946115, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1438, Total reward: 273.11218134927395, Average Rewards: 240.05779265499513, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1439, Total reward: 262.16432100396185, Average Rewards: 240.01633224880246, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1440, Total reward: 263.5341484193802, Average Rewards: 240.0513397012219, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1441, Total reward: 252.19035016839462, Average Rewards: 240.28113587686647, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1442, Total reward: 275.89680240688597, Average Rewards: 239.97285675828684, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1443, Total reward: 245.58396828100845, Average Rewards: 240.1791209497381, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1444, Total reward: 271.0947020499583, Average Rewards: 240.07564948155468, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1445, Total reward: 251.9807291996675, Average Rewards: 240.3782663405831, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1446, Total reward: -53.04255348075357, Average Rewards: 240.3908107431456, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1447, Total reward: 290.99410480965025, Average Rewards: 237.22747228386262, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1448, Total reward: -127.94210351921787, Average Rewards: 237.52620716934223, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1449, Total reward: 234.5183401130102, Average Rewards: 233.60009385661462, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1450, Total reward: 265.5293702643369, Average Rewards: 233.33294045375445, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1451, Total reward: 262.6381915439357, Average Rewards: 233.57360784431864, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1452, Total reward: 240.9934942791352, Average Rewards: 233.56754141834531, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1453, Total reward: 283.1644583125564, Average Rewards: 233.20863658441777, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1454, Total reward: 262.9503436481821, Average Rewards: 233.556046185108, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1455, Total reward: 272.3345150899552, Average Rewards: 233.5112332409219, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1456, Total reward: -35.52600284949942, Average Rewards: 233.726498859237, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1457, Total reward: 252.72313072924538, Average Rewards: 231.2846367933025, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1458, Total reward: 232.0557059442544, Average Rewards: 231.44662337831957, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1459, Total reward: 258.3951931069586, Average Rewards: 230.97413876197805, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1460, Total reward: 274.8265289884764, Average Rewards: 230.97808261245981, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1461, Total reward: 254.03792233270642, Average Rewards: 231.1928319942963, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1462, Total reward: 244.69666987401746, Average Rewards: 231.38562495431717, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1463, Total reward: 248.4973698767977, Average Rewards: 231.3942447395213, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1464, Total reward: 266.9341566613347, Average Rewards: 231.13349581088448, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1465, Total reward: 288.4707813738386, Average Rewards: 231.29627954264535, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1466, Total reward: 284.76953238108365, Average Rewards: 231.194515238248, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1467, Total reward: 239.80629081923567, Average Rewards: 231.409403034094, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1468, Total reward: 295.9560224356661, Average Rewards: 231.09562180225748, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1469, Total reward: 238.96169234850865, Average Rewards: 231.22460422475598, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1470, Total reward: 277.550324516311, Average Rewards: 233.8926382997923, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1471, Total reward: 279.73515903973475, Average Rewards: 236.68666958959093, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1472, Total reward: 286.38852127366874, Average Rewards: 236.80354237459417, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1473, Total reward: -48.54238553308141, Average Rewards: 237.00326397577956, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1474, Total reward: 282.7053182826251, Average Rewards: 233.81699251943047, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1475, Total reward: 287.51721463974957, Average Rewards: 233.89701777571992, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1476, Total reward: 295.84351985495584, Average Rewards: 236.70872561829262, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1477, Total reward: 240.94343456798984, Average Rewards: 237.36387499766394, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1478, Total reward: 274.57554180334176, Average Rewards: 237.58671275521345, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1479, Total reward: 283.4605401400865, Average Rewards: 237.53497072321807, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1480, Total reward: 285.6830940233617, Average Rewards: 237.75994127696504, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1481, Total reward: 306.87814429565725, Average Rewards: 238.15656381978863, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1482, Total reward: 283.0485278488448, Average Rewards: 238.63523367028367, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1483, Total reward: 295.5542755842348, Average Rewards: 239.0678128094327, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1484, Total reward: 292.37473914553243, Average Rewards: 239.60633051215493, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1485, Total reward: 257.2210382622404, Average Rewards: 240.2279867147091, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1486, Total reward: 260.9746560350071, Average Rewards: 240.20502998893892, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1487, Total reward: 286.5602748289177, Average Rewards: 240.2862325315372, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1488, Total reward: 300.4289282309359, Average Rewards: 240.19711312814093, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1489, Total reward: 265.1358604611838, Average Rewards: 240.53530098502918, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1490, Total reward: 266.9447749368892, Average Rewards: 240.46007617790477, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1491, Total reward: 300.56516643660126, Average Rewards: 240.38699547306842, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1492, Total reward: 232.02730765595925, Average Rewards: 241.1623223413143, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1493, Total reward: 272.01788041753605, Average Rewards: 240.9376960539203, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1494, Total reward: 268.17833936611305, Average Rewards: 241.2770548821989, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1495, Total reward: 293.99308716397434, Average Rewards: 241.15451925909176, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1496, Total reward: 275.1131263437865, Average Rewards: 244.40965639134967, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1497, Total reward: 261.1492475179306, Average Rewards: 244.616437288806, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1498, Total reward: 290.6540992031529, Average Rewards: 244.53100252486206, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1499, Total reward: 268.98316120804594, Average Rewards: 244.85215887430695, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1500, Total reward: 265.28953652889027, Average Rewards: 244.7407888816968, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1501, Total reward: 258.09134971570194, Average Rewards: 244.78198606472958, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1502, Total reward: 247.40352512297832, Average Rewards: 244.79893053042997, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1503, Total reward: -48.559970859888296, Average Rewards: 244.91285760407936, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1504, Total reward: 269.54860783705027, Average Rewards: 241.6818592185696, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1505, Total reward: 298.619900575423, Average Rewards: 241.8314302038115, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1506, Total reward: 235.73218051204304, Average Rewards: 242.24484405430806, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1507, Total reward: 264.49419605475777, Average Rewards: 241.7166429311519, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1508, Total reward: 273.8964292651219, Average Rewards: 241.55066406977835, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1509, Total reward: 262.3279234683614, Average Rewards: 241.7652745512461, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1510, Total reward: 302.2852957068039, Average Rewards: 241.85093150159588, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1511, Total reward: 264.7677119289528, Average Rewards: 242.13514966258919, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1512, Total reward: 239.51463572611905, Average Rewards: 242.0871100621035, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1513, Total reward: 296.01031955403545, Average Rewards: 241.8644244198788, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1514, Total reward: 247.52930935268026, Average Rewards: 242.45813826533487, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1515, Total reward: 229.01626011517573, Average Rewards: 242.2874643644504, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1516, Total reward: 277.77738490271474, Average Rewards: 241.93973734739254, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1517, Total reward: 281.90913896085715, Average Rewards: 247.17860890489504, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1518, Total reward: 269.18979259805474, Average Rewards: 247.27476084680046, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1519, Total reward: -57.48262268945004, Average Rewards: 247.58800151290268, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1520, Total reward: 276.0816243830709, Average Rewards: 244.6759650998651, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1521, Total reward: -55.10248245888191, Average Rewards: 244.65598872663412, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1522, Total reward: 259.2734176502422, Average Rewards: 241.59906285788367, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1523, Total reward: 305.3604994081378, Average Rewards: 241.613210508023, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1524, Total reward: -40.61710351427316, Average Rewards: 241.90974751276164, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1525, Total reward: 279.78168582810844, Average Rewards: 241.88963801287602, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1526, Total reward: 245.8533643952593, Average Rewards: 242.17293749676165, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1527, Total reward: 271.5968070189814, Average Rewards: 242.23631629703294, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1528, Total reward: 272.5765037052506, Average Rewards: 242.37037567356379, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1529, Total reward: 247.39444630297137, Average Rewards: 242.51141390576868, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1530, Total reward: 261.0595716451128, Average Rewards: 242.564073557644, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1531, Total reward: 246.5864055843974, Average Rewards: 242.8197257688581, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1532, Total reward: 261.31089012614643, Average Rewards: 242.5284219293758, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1533, Total reward: 242.40992065626028, Average Rewards: 242.24743608134256, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1534, Total reward: 215.68704512936526, Average Rewards: 241.74878581047125, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1535, Total reward: 275.76380020812496, Average Rewards: 241.4632054122371, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1536, Total reward: 255.57245120004234, Average Rewards: 241.66472689410483, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1537, Total reward: 238.5776763777992, Average Rewards: 241.31270306845377, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1538, Total reward: 244.18561015396392, Average Rewards: 241.3249070353573, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1539, Total reward: 236.17188276848037, Average Rewards: 241.03564132340415, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1540, Total reward: 253.7744788973341, Average Rewards: 240.77571694104938, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1541, Total reward: 273.65288283670213, Average Rewards: 240.6781202458289, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1542, Total reward: 234.14387860277117, Average Rewards: 240.89274557251198, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1543, Total reward: 232.07657739806922, Average Rewards: 240.47521633447082, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1544, Total reward: 240.20768526552447, Average Rewards: 240.34014242564143, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1545, Total reward: 280.2139708776381, Average Rewards: 240.0312722577971, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1546, Total reward: 280.43797007426457, Average Rewards: 240.31360467457682, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1547, Total reward: 237.0510711035481, Average Rewards: 243.64840991012693, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1548, Total reward: 267.74258332274917, Average Rewards: 243.108979573066, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1549, Total reward: 244.51742617913723, Average Rewards: 247.06582644148568, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1550, Total reward: 228.64550248551058, Average Rewards: 247.16581730214693, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1551, Total reward: 243.7379657168245, Average Rewards: 246.79697862435864, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1552, Total reward: 252.69504741246416, Average Rewards: 246.60797636608754, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1553, Total reward: 254.69965753909602, Average Rewards: 246.7249918974208, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1554, Total reward: 261.3714002789711, Average Rewards: 246.4403438896862, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1555, Total reward: 83.58134765965698, Average Rewards: 246.4245544559941, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1556, Total reward: 252.11231563980093, Average Rewards: 244.53702278169112, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1557, Total reward: 279.8566855023905, Average Rewards: 247.41340596658415, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1558, Total reward: 272.5358682481493, Average Rewards: 247.68474151431556, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1559, Total reward: 265.1540256823058, Average Rewards: 248.08954313735455, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1560, Total reward: 277.5379941344105, Average Rewards: 248.15713146310804, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1561, Total reward: 173.75642915055886, Average Rewards: 248.18424611456737, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1562, Total reward: 239.74862332495843, Average Rewards: 247.3814311827459, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1563, Total reward: 286.88804770764546, Average Rewards: 247.33195071725532, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1564, Total reward: 224.6957452262506, Average Rewards: 247.71585749556377, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1565, Total reward: 268.7623710689804, Average Rewards: 247.29347338121295, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1566, Total reward: 268.8789088227571, Average Rewards: 247.09638927816434, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1567, Total reward: 276.17254207876226, Average Rewards: 246.93748304258108, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1568, Total reward: 257.7074502483929, Average Rewards: 247.30114555517633, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1569, Total reward: 279.38626104712137, Average Rewards: 246.91865983330362, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1570, Total reward: 281.45305241104904, Average Rewards: 247.32290552028974, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1571, Total reward: 264.15558833920477, Average Rewards: 247.36193279923714, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1572, Total reward: 212.94237233538846, Average Rewards: 247.2061370922318, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1573, Total reward: 237.6450625206235, Average Rewards: 246.47167560284907, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1574, Total reward: 260.0919874615831, Average Rewards: 249.33355008338611, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1575, Total reward: 240.9327564741332, Average Rewards: 249.10741677517566, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1576, Total reward: 255.9179846832669, Average Rewards: 248.6415721935195, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1577, Total reward: 268.0438833535054, Average Rewards: 248.24231684180256, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1578, Total reward: 272.62203472395476, Average Rewards: 248.51332132965774, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1579, Total reward: 229.2113353788884, Average Rewards: 248.49378625886388, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1580, Total reward: 278.71392444832475, Average Rewards: 247.9512942112519, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1581, Total reward: 229.78906209134, Average Rewards: 247.88160251550156, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1582, Total reward: 262.10281082272485, Average Rewards: 247.11071169345837, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1583, Total reward: 272.50755777627836, Average Rewards: 246.9012545231972, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1584, Total reward: 259.79530707458093, Average Rewards: 246.67078734511765, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1585, Total reward: 264.1095203984048, Average Rewards: 246.34499302440815, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1586, Total reward: 242.16483686431658, Average Rewards: 246.41387784576978, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1587, Total reward: 268.14154860184516, Average Rewards: 246.22577965406288, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1588, Total reward: 288.93622315034895, Average Rewards: 246.04159239179214, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1589, Total reward: 267.52546509572227, Average Rewards: 245.92666534098623, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1590, Total reward: 257.76460309092204, Average Rewards: 245.95056138733162, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1591, Total reward: 242.259369303137, Average Rewards: 245.85875966887193, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1592, Total reward: 272.7218532212539, Average Rewards: 245.2757016975373, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1593, Total reward: 259.98207461800837, Average Rewards: 245.6826471531903, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1594, Total reward: 271.93867956724483, Average Rewards: 245.562289095195, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1595, Total reward: 283.15871692990527, Average Rewards: 245.59989249720627, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1596, Total reward: 241.9859934980524, Average Rewards: 245.4915487948656, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1597, Total reward: 130.66073581693854, Average Rewards: 245.16027746640827, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1598, Total reward: 255.37397505943628, Average Rewards: 243.85539234939836, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1599, Total reward: 265.38552263781753, Average Rewards: 243.5025911079612, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1600, Total reward: 301.0384485053799, Average Rewards: 243.4666147222589, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1601, Total reward: 265.6330376394206, Average Rewards: 243.8241038420238, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1602, Total reward: 285.15360269534955, Average Rewards: 243.89952072126098, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1603, Total reward: 269.64966011451463, Average Rewards: 244.2770214969847, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1604, Total reward: 302.53117876989177, Average Rewards: 247.45911780672876, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1605, Total reward: 258.9521332750095, Average Rewards: 247.7889435160572, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1606, Total reward: 256.2892442178959, Average Rewards: 247.392265843053, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1607, Total reward: 260.9236529479655, Average Rewards: 247.59783648011154, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1608, Total reward: 236.12070141521798, Average Rewards: 247.56213104904356, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1609, Total reward: 263.152307257243, Average Rewards: 247.18437377054454, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1610, Total reward: 305.71754656871644, Average Rewards: 247.19261760843332, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1611, Total reward: 241.91792201424417, Average Rewards: 247.22694011705252, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1612, Total reward: 284.8182180792907, Average Rewards: 246.9984422179054, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1613, Total reward: 252.72927723824282, Average Rewards: 247.4514780414371, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1614, Total reward: 236.8669758670092, Average Rewards: 247.01866761827918, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1615, Total reward: 260.2651802968171, Average Rewards: 246.91204428342243, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1616, Total reward: 247.79848069924225, Average Rewards: 247.2245334852389, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1617, Total reward: 285.19767251476935, Average Rewards: 246.92474444320413, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1618, Total reward: 286.24654953683176, Average Rewards: 246.9576297787433, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1619, Total reward: 282.08342416538176, Average Rewards: 247.12819734813107, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1620, Total reward: 257.85064550153265, Average Rewards: 250.52385781667942, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1621, Total reward: 271.0095425165183, Average Rewards: 250.34154802786404, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1622, Total reward: 287.09275321117843, Average Rewards: 253.60266827761808, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1623, Total reward: 235.12511297283464, Average Rewards: 253.88086163322743, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1624, Total reward: 247.73252817767687, Average Rewards: 253.17850776887437, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1625, Total reward: 270.42078093520456, Average Rewards: 256.0620040857939, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1626, Total reward: 270.39776703850407, Average Rewards: 255.9683950368648, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1627, Total reward: 256.3148162754054, Average Rewards: 256.21383906329726, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1628, Total reward: 266.25775438107416, Average Rewards: 256.06101915586146, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1629, Total reward: 278.05967556624006, Average Rewards: 255.99783166261972, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1630, Total reward: 280.0493846673068, Average Rewards: 256.3044839552524, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1631, Total reward: 271.8361135673632, Average Rewards: 256.49438208547434, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1632, Total reward: 268.185448267227, Average Rewards: 256.746879165304, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1633, Total reward: 261.24421129018253, Average Rewards: 256.81562474671483, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1634, Total reward: 297.34364554812106, Average Rewards: 257.003967653054, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1635, Total reward: 267.912856766563, Average Rewards: 257.8205336572416, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1636, Total reward: 280.6247960923704, Average Rewards: 257.742024222826, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1637, Total reward: 304.96881844799964, Average Rewards: 257.99254767174926, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1638, Total reward: 264.1936212027874, Average Rewards: 258.6564590924513, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1639, Total reward: 249.74118460594053, Average Rewards: 258.8565392029395, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1640, Total reward: 266.766207118633, Average Rewards: 258.99223222131417, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1641, Total reward: 267.5907821484101, Average Rewards: 259.12214950352706, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1642, Total reward: 264.50899808156305, Average Rewards: 259.06152849664414, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1643, Total reward: 262.22519304889784, Average Rewards: 259.3651796914321, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1644, Total reward: 260.4518673130525, Average Rewards: 259.6666658479404, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1645, Total reward: 267.3494808931183, Average Rewards: 259.86910766841567, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1646, Total reward: 270.29399390642163, Average Rewards: 259.74046276857047, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1647, Total reward: 256.65546377690487, Average Rewards: 259.639023006892, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1648, Total reward: 242.6301250495836, Average Rewards: 259.83506693362557, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1649, Total reward: 293.33936339887106, Average Rewards: 259.58394235089395, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1650, Total reward: 256.93377607079765, Average Rewards: 260.0721617230913, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1651, Total reward: 263.09481518147584, Average Rewards: 260.3550444589442, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1652, Total reward: 293.0754902559776, Average Rewards: 260.5486129535907, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1653, Total reward: 287.0790670443721, Average Rewards: 260.9524173820258, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1654, Total reward: 267.6825824641589, Average Rewards: 261.27621147707856, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1655, Total reward: 279.5618010592754, Average Rewards: 261.3393232989305, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1656, Total reward: 289.2642504160226, Average Rewards: 263.2991278329266, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1657, Total reward: 262.5160385753154, Average Rewards: 263.6706471806889, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1658, Total reward: 272.8273828328897, Average Rewards: 263.4972407114181, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1659, Total reward: 284.625937498959, Average Rewards: 263.50015585726555, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1660, Total reward: 234.45742275556432, Average Rewards: 263.694874975432, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1661, Total reward: 257.2715374901476, Average Rewards: 263.26406926164356, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1662, Total reward: 252.15018588202767, Average Rewards: 264.09922034503944, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1663, Total reward: 278.9777944082005, Average Rewards: 264.2232359706102, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1664, Total reward: 290.5570283319978, Average Rewards: 264.1441334376157, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1665, Total reward: 232.88771312121295, Average Rewards: 264.80274626867316, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1666, Total reward: 292.8227135735483, Average Rewards: 264.4439996891955, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1667, Total reward: 320.5272569650718, Average Rewards: 264.68343773670335, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1668, Total reward: 287.72278073214125, Average Rewards: 265.1269848855665, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1669, Total reward: 237.09559012739135, Average Rewards: 265.427138190404, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1670, Total reward: 294.25394730512187, Average Rewards: 265.0042314812067, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1671, Total reward: 253.21365093570836, Average Rewards: 265.1322404301474, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1672, Total reward: 272.1372004474454, Average Rewards: 265.02282105611243, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1673, Total reward: 273.2701394259702, Average Rewards: 265.614769337233, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1674, Total reward: 287.6551356192449, Average Rewards: 265.9710201062865, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1675, Total reward: 227.45130257531554, Average Rewards: 266.2466515878631, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1676, Total reward: 262.0751977771538, Average Rewards: 266.111837048875, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1677, Total reward: 276.9153083446992, Average Rewards: 266.1734091798138, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1678, Total reward: 234.16464299096862, Average Rewards: 266.2621234297257, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1679, Total reward: 281.51499140233045, Average Rewards: 265.87754951239583, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1680, Total reward: 223.92115335169035, Average Rewards: 266.4005860726303, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1681, Total reward: 283.96277122384663, Average Rewards: 265.85265836166394, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1682, Total reward: 285.6726100885232, Average Rewards: 266.394395452989, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1683, Total reward: 271.61126048357835, Average Rewards: 266.63009344564705, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1684, Total reward: 302.0205288133748, Average Rewards: 266.62113047272004, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1685, Total reward: 248.93424643837548, Average Rewards: 267.043382690108, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1686, Total reward: 266.20514801991516, Average Rewards: 266.89162995050776, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1687, Total reward: 296.4155876463832, Average Rewards: 267.1320330620637, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1688, Total reward: 295.3143587265446, Average Rewards: 267.41477345250905, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1689, Total reward: 282.07293521968984, Average Rewards: 267.478554808271, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1690, Total reward: 261.58444252486106, Average Rewards: 267.6240295095107, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1691, Total reward: 249.3789562867713, Average Rewards: 267.66222790385007, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1692, Total reward: 257.96417151626974, Average Rewards: 267.7334237736864, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1693, Total reward: 302.5369476344836, Average Rewards: 267.58584695663654, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1694, Total reward: 279.8826579671621, Average Rewards: 268.0113956868013, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1695, Total reward: 218.55426164495003, Average Rewards: 268.09083547080047, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1696, Total reward: 273.3596447417824, Average Rewards: 267.4447909179509, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1697, Total reward: 230.62878816399797, Average Rewards: 267.7585274303882, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1698, Total reward: 295.4398737500681, Average Rewards: 268.7582079538588, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1699, Total reward: 306.69937413487537, Average Rewards: 269.1588669407651, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1700, Total reward: 234.09789129353842, Average Rewards: 269.5720054557357, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1701, Total reward: 266.9061837556249, Average Rewards: 268.9025998836173, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1702, Total reward: 274.03068236820684, Average Rewards: 268.91533134477936, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1703, Total reward: 241.88611851816512, Average Rewards: 268.8041021415079, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1704, Total reward: 277.06566274098253, Average Rewards: 268.5264667255444, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1705, Total reward: 281.5233677767844, Average Rewards: 268.27181156525535, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1706, Total reward: 287.4945715906307, Average Rewards: 268.49752391027306, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1707, Total reward: 265.2592454290794, Average Rewards: 268.80957718400043, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1708, Total reward: 291.15367570206627, Average Rewards: 268.8529331088115, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1709, Total reward: 275.54974200949283, Average Rewards: 269.40326285168, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1710, Total reward: 281.12753678620703, Average Rewards: 269.52723719920255, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1711, Total reward: 266.6610781024616, Average Rewards: 269.2813371013774, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1712, Total reward: 245.11471045532835, Average Rewards: 269.5287686622596, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1713, Total reward: 274.0131803596016, Average Rewards: 269.13173358602, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1714, Total reward: 279.63346810946416, Average Rewards: 269.34457261723355, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1715, Total reward: 266.06805701463975, Average Rewards: 269.77223753965814, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1716, Total reward: 279.50538090654806, Average Rewards: 269.83026630683634, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1717, Total reward: 256.4909134296708, Average Rewards: 270.14733530890936, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1718, Total reward: 248.02529734652902, Average Rewards: 269.86026771805837, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1719, Total reward: 278.5713968884106, Average Rewards: 269.4780551961554, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1720, Total reward: 294.6420918813471, Average Rewards: 269.4429349233857, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1721, Total reward: 282.81844449075425, Average Rewards: 269.8108493871838, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1722, Total reward: 290.45106310753135, Average Rewards: 269.9289384069262, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1723, Total reward: 300.7108564308786, Average Rewards: 269.9625215058897, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1724, Total reward: 232.2596520262835, Average Rewards: 270.61837894047017, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1725, Total reward: 294.06376153200745, Average Rewards: 270.46365017895624, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1726, Total reward: 286.0779717394021, Average Rewards: 270.7000799849243, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1727, Total reward: 248.57089298292175, Average Rewards: 270.8568820319333, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1728, Total reward: 293.2911827508727, Average Rewards: 270.77944279900845, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1729, Total reward: 294.79793513240486, Average Rewards: 271.04977708270644, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1730, Total reward: 301.9895285542983, Average Rewards: 271.2171596783681, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1731, Total reward: 249.782142989617, Average Rewards: 271.436561117238, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1732, Total reward: 248.6316907348973, Average Rewards: 271.2160214114605, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1733, Total reward: 299.9323708676731, Average Rewards: 271.02048383613715, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1734, Total reward: 304.03592885824946, Average Rewards: 271.4073654319121, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1735, Total reward: 231.2214982636852, Average Rewards: 271.4742882650134, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1736, Total reward: 270.20641920214126, Average Rewards: 271.10737467998456, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1737, Total reward: 260.09436103453174, Average Rewards: 271.00319091108236, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1738, Total reward: 256.4562315339572, Average Rewards: 270.55444633694765, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1739, Total reward: 257.6197748431422, Average Rewards: 270.47707244025935, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1740, Total reward: -178.91381731900668, Average Rewards: 270.5558583426313, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1741, Total reward: 305.1904356746453, Average Rewards: 266.0990580982549, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1742, Total reward: 290.82422887357586, Average Rewards: 266.47505463351723, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1743, Total reward: 269.48799258636063, Average Rewards: 266.7382069414374, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1744, Total reward: 296.41462842038226, Average Rewards: 266.810834936812, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1745, Total reward: 259.82735202816787, Average Rewards: 267.17046254788534, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1746, Total reward: 268.60801279866257, Average Rewards: 267.0952412592358, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1747, Total reward: 246.75740179065767, Average Rewards: 267.07838144815827, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1748, Total reward: 280.91745017329674, Average Rewards: 266.9794008282958, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1749, Total reward: 271.96075896298316, Average Rewards: 267.36227407953294, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1750, Total reward: 257.9987736041772, Average Rewards: 267.14848803517407, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1751, Total reward: 237.25334179649744, Average Rewards: 267.15913801050783, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1752, Total reward: 259.55262681206915, Average Rewards: 266.900723276658, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1753, Total reward: 285.44345474001625, Average Rewards: 266.56549464221894, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1754, Total reward: 265.6233957960539, Average Rewards: 266.5491385191754, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1755, Total reward: 231.83979049869788, Average Rewards: 266.52854665249436, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1756, Total reward: 233.52622853821703, Average Rewards: 266.0513265468886, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1757, Total reward: 244.4519235028035, Average Rewards: 265.49394632811055, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1758, Total reward: 308.28963717054467, Average Rewards: 265.31330517738536, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1759, Total reward: 284.8182362028815, Average Rewards: 265.66792772076195, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1760, Total reward: 295.36604987027636, Average Rewards: 265.6698507078012, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1761, Total reward: 227.8952483880321, Average Rewards: 266.2789369789483, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1762, Total reward: 256.015309826382, Average Rewards: 265.98517408792713, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1763, Total reward: 277.02871362541794, Average Rewards: 266.0238253273707, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1764, Total reward: 283.8097485965865, Average Rewards: 266.00433451954285, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1765, Total reward: 236.47108618385494, Average Rewards: 265.9368617221888, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1766, Total reward: 218.3423992470315, Average Rewards: 265.9726954528152, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1767, Total reward: 242.7894255846852, Average Rewards: 265.22789230955, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1768, Total reward: 257.12138478611917, Average Rewards: 264.45051399574606, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1769, Total reward: 250.96023155006515, Average Rewards: 264.1445000362859, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1770, Total reward: 286.86370005852655, Average Rewards: 264.28314645051256, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1771, Total reward: 240.69048989661587, Average Rewards: 264.20924397804663, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1772, Total reward: 252.3225727439815, Average Rewards: 264.08401236765576, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1773, Total reward: 266.6130021478832, Average Rewards: 263.8858660906211, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1774, Total reward: 284.48441761388654, Average Rewards: 263.81929471784025, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1775, Total reward: 221.1231937844834, Average Rewards: 263.7875875377867, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1776, Total reward: 258.0633580830453, Average Rewards: 263.7243064498783, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1777, Total reward: 277.46198006583506, Average Rewards: 263.68418805293726, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1778, Total reward: 314.13263301245496, Average Rewards: 263.6896547701486, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1779, Total reward: 236.2439454922857, Average Rewards: 264.48933467036346, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1780, Total reward: 183.4979870784329, Average Rewards: 264.036624211263, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1781, Total reward: 310.35186378957667, Average Rewards: 263.6323925485305, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1782, Total reward: 275.9106444760739, Average Rewards: 263.8962834741878, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1783, Total reward: 280.9848128537717, Average Rewards: 263.7986638180633, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1784, Total reward: 284.8873034570374, Average Rewards: 263.8923993417652, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1785, Total reward: 307.87161438650725, Average Rewards: 263.72106708820183, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1786, Total reward: 247.58040665167977, Average Rewards: 264.31044076768313, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1787, Total reward: 312.56995141968287, Average Rewards: 264.1241933540008, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1788, Total reward: 281.3119212741791, Average Rewards: 264.2857369917338, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1789, Total reward: 238.62978294079167, Average Rewards: 264.1457126172101, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1790, Total reward: 276.11852902575504, Average Rewards: 263.7112810944211, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1791, Total reward: 236.68172391053616, Average Rewards: 263.85662195943013, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1792, Total reward: 298.2444348976786, Average Rewards: 263.7296496356678, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1793, Total reward: 241.0620020065657, Average Rewards: 264.13245226948186, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1794, Total reward: 269.3027989806857, Average Rewards: 263.5177028132027, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1795, Total reward: 296.6265073486357, Average Rewards: 263.4119042233379, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1796, Total reward: 271.1024927024125, Average Rewards: 264.19262668037476, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1797, Total reward: 295.4961260754121, Average Rewards: 264.1700551599811, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1798, Total reward: 308.2337086311272, Average Rewards: 264.8187285390952, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1799, Total reward: 218.3070914190721, Average Rewards: 264.9466668879058, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1800, Total reward: 280.62114004575733, Average Rewards: 264.0627440607478, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1801, Total reward: 247.07573881770975, Average Rewards: 264.52797654827, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1802, Total reward: 268.23767330293646, Average Rewards: 264.3296720988908, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1803, Total reward: 290.48189481027765, Average Rewards: 264.27174200823805, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1804, Total reward: 293.8764760096879, Average Rewards: 264.7576997711592, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1805, Total reward: 246.15084758823016, Average Rewards: 264.9258079038463, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1806, Total reward: 294.7532394258071, Average Rewards: 264.57208270196077, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1807, Total reward: 241.7500600740385, Average Rewards: 264.6446693803125, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1808, Total reward: 281.37144083484054, Average Rewards: 264.40957752676206, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1809, Total reward: 253.47732942399273, Average Rewards: 264.3117551780898, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1810, Total reward: 282.06645739626447, Average Rewards: 264.0910310522348, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1811, Total reward: 304.88719216120614, Average Rewards: 264.10042025833536, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1812, Total reward: 304.760737849697, Average Rewards: 264.4826813989228, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1813, Total reward: 260.0784840198721, Average Rewards: 265.0791416728665, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1814, Total reward: 244.85617290094615, Average Rewards: 264.9397947094692, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1815, Total reward: 247.38117463318815, Average Rewards: 264.59202175738403, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1816, Total reward: 284.83492279438155, Average Rewards: 264.4051529335695, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1817, Total reward: 226.33005520352813, Average Rewards: 264.4584483524479, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1818, Total reward: 245.16818372572945, Average Rewards: 264.1568397701864, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1819, Total reward: 240.1079136631009, Average Rewards: 264.12826863397845, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1820, Total reward: 267.3317979497338, Average Rewards: 263.74363380172537, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1821, Total reward: 275.05323428446474, Average Rewards: 263.4705308624092, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1822, Total reward: 263.38828397324176, Average Rewards: 263.3928787603463, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1823, Total reward: 239.99121262737089, Average Rewards: 263.1222509690034, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1824, Total reward: 222.22914364130935, Average Rewards: 262.5150545309683, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1825, Total reward: 302.59164415941063, Average Rewards: 262.41474944711865, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1826, Total reward: 292.0292108691167, Average Rewards: 262.5000282733926, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1827, Total reward: 247.33755453813367, Average Rewards: 262.5595406646898, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1828, Total reward: 289.6889103008415, Average Rewards: 262.54720728024193, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1829, Total reward: 304.20885925955315, Average Rewards: 262.5111845557416, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1830, Total reward: 243.59553695375732, Average Rewards: 262.6052937970131, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1831, Total reward: 246.3343187955233, Average Rewards: 262.0213538810077, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1832, Total reward: 284.76574432068037, Average Rewards: 261.98687563906674, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1833, Total reward: 241.88956492596392, Average Rewards: 262.3482161749246, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1834, Total reward: 238.95880949271478, Average Rewards: 261.7677881155075, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1835, Total reward: 273.05043231434524, Average Rewards: 261.1170169218521, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1836, Total reward: 284.45417834227896, Average Rewards: 261.53530626235874, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1837, Total reward: 236.54156336545046, Average Rewards: 261.6777838537601, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1838, Total reward: 264.93635954926367, Average Rewards: 261.4422558770693, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1839, Total reward: 296.71318945202137, Average Rewards: 261.52705715722243, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1840, Total reward: 300.3604705915644, Average Rewards: 261.91799130331117, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1841, Total reward: 286.41104300087824, Average Rewards: 266.7107341824169, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1842, Total reward: 243.86530506920386, Average Rewards: 266.5229402556792, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1843, Total reward: 250.01233836709466, Average Rewards: 266.05335101763546, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1844, Total reward: 257.0841920162972, Average Rewards: 265.8585944754428, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1845, Total reward: 227.6418817515946, Average Rewards: 265.46529011140194, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1846, Total reward: 265.4307163642476, Average Rewards: 265.1434354086362, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1847, Total reward: 263.6356752302863, Average Rewards: 265.1116624442921, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1848, Total reward: 282.19009613148717, Average Rewards: 265.28044517868835, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1849, Total reward: 265.8863814391418, Average Rewards: 265.2931716382702, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1850, Total reward: 286.1021210357861, Average Rewards: 265.23242786303183, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1851, Total reward: 150.39302318839287, Average Rewards: 265.5134613373479, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1852, Total reward: 305.494690450755, Average Rewards: 264.6448581512669, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1853, Total reward: 277.42841752941285, Average Rewards: 265.10427878765375, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1854, Total reward: 258.6847133858274, Average Rewards: 265.02412841554775, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1855, Total reward: 245.61173418989495, Average Rewards: 264.95474159144544, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1856, Total reward: 276.12675080417523, Average Rewards: 265.09246102835743, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1857, Total reward: 252.15079534548946, Average Rewards: 265.518466251017, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1858, Total reward: 297.5989399064323, Average Rewards: 265.59545496944384, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1859, Total reward: 247.911241788071, Average Rewards: 265.4885479968027, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1860, Total reward: 283.0335151520212, Average Rewards: 265.11947805265464, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1861, Total reward: 228.85597867153012, Average Rewards: 264.99615270547207, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1862, Total reward: 253.9218568298686, Average Rewards: 265.0057600083071, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1863, Total reward: 261.71238199065147, Average Rewards: 264.9848254783419, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1864, Total reward: 271.5211459529568, Average Rewards: 264.83166216199425, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1865, Total reward: 273.0526326766163, Average Rewards: 264.70877613555797, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1866, Total reward: 312.9202146790376, Average Rewards: 265.0745916004855, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1867, Total reward: 243.38147319467345, Average Rewards: 266.02036975480564, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1868, Total reward: 300.2589128775174, Average Rewards: 266.0262902309055, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1869, Total reward: 271.6825375841315, Average Rewards: 266.4576655118195, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1870, Total reward: 280.47998908965894, Average Rewards: 266.6648885721602, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1871, Total reward: 265.08829894045346, Average Rewards: 266.6010514624715, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1872, Total reward: 289.8383569175485, Average Rewards: 266.84502955290986, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1873, Total reward: 249.98737070278688, Average Rewards: 267.2201873946455, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1874, Total reward: 279.261759703368, Average Rewards: 267.0539310801946, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1875, Total reward: 264.3939408680666, Average Rewards: 267.00170450108936, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1876, Total reward: 270.4950745213009, Average Rewards: 267.4344119719252, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1877, Total reward: 251.45587330702932, Average Rewards: 267.55872913630776, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1878, Total reward: 288.8866249401344, Average Rewards: 267.29866806871973, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1879, Total reward: 259.69132761209283, Average Rewards: 267.04620798799647, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1880, Total reward: 289.96340660356515, Average Rewards: 267.2806818091946, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1881, Total reward: 280.3701126145487, Average Rewards: 268.34533600444587, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1882, Total reward: 275.9388316194056, Average Rewards: 268.04551849269563, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1883, Total reward: 300.59377327219613, Average Rewards: 268.045800364129, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1884, Total reward: 267.3539977827124, Average Rewards: 268.2418899683132, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1885, Total reward: 296.0797358345238, Average Rewards: 268.06655691157, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1886, Total reward: 300.95714143712337, Average Rewards: 267.9486381260501, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1887, Total reward: 288.20777248514037, Average Rewards: 268.4824054739046, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1888, Total reward: 269.5486074546543, Average Rewards: 268.2387836845591, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1889, Total reward: 263.67866640270927, Average Rewards: 268.1211505463639, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1890, Total reward: 282.3915775555305, Average Rewards: 268.3716393809831, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1891, Total reward: 314.92162312815447, Average Rewards: 268.4343698662808, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1892, Total reward: 297.93503275937564, Average Rewards: 269.216768858457, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1893, Total reward: 315.7935352611231, Average Rewards: 269.21367483707394, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1894, Total reward: 289.5356872962117, Average Rewards: 269.9609901696195, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1895, Total reward: 285.0343331695823, Average Rewards: 270.16331905277474, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1896, Total reward: 257.37462049351484, Average Rewards: 270.04739731098425, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1897, Total reward: 261.09605554618247, Average Rewards: 269.91011858889533, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1898, Total reward: 262.10182362576313, Average Rewards: 269.566117883603, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1899, Total reward: 265.5457520309373, Average Rewards: 269.10479903354934, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1900, Total reward: 267.8603977294126, Average Rewards: 269.57718563966796, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1901, Total reward: 265.2922971252468, Average Rewards: 269.4495782165045, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1902, Total reward: 274.77532675100736, Average Rewards: 269.6317437995799, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1903, Total reward: 304.8307392785359, Average Rewards: 269.6971203340606, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1904, Total reward: 264.10377718475166, Average Rewards: 269.8406087787432, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1905, Total reward: 290.4551000829581, Average Rewards: 269.5428817904938, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1906, Total reward: 266.21933481384167, Average Rewards: 269.98592431544114, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1907, Total reward: 271.93339208000145, Average Rewards: 269.7005852693215, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1908, Total reward: 256.498641554781, Average Rewards: 270.00241858938114, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1909, Total reward: 276.3652733622879, Average Rewards: 269.75369059658055, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1910, Total reward: 304.3773832992514, Average Rewards: 269.98257003596353, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1911, Total reward: 268.8963807667511, Average Rewards: 270.20567929499333, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1912, Total reward: 270.4755301937598, Average Rewards: 269.8457711810488, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1913, Total reward: 278.223820737384, Average Rewards: 269.50291910448937, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1914, Total reward: 278.6866114292351, Average Rewards: 269.6843724716645, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1915, Total reward: 305.91433752180365, Average Rewards: 270.0226768569474, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1916, Total reward: 262.56259918367687, Average Rewards: 270.6080084858336, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1917, Total reward: 310.06567995097123, Average Rewards: 270.3852852497265, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1918, Total reward: 273.24632622512087, Average Rewards: 271.222641497201, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1919, Total reward: 270.2525945497096, Average Rewards: 271.5034229221949, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1920, Total reward: 249.62289213275074, Average Rewards: 271.804869731061, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1921, Total reward: 280.7891813995264, Average Rewards: 271.62778067289116, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1922, Total reward: 257.6542542943131, Average Rewards: 271.6851401440418, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1923, Total reward: 299.98289824211497, Average Rewards: 271.6277998472525, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1924, Total reward: 275.007920424521, Average Rewards: 272.22771670339984, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1925, Total reward: 283.165742051953, Average Rewards: 272.755504471232, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1926, Total reward: 277.5171784933183, Average Rewards: 272.5612454501574, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1927, Total reward: 300.2203789470431, Average Rewards: 272.41612512639944, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1928, Total reward: 216.49066609950557, Average Rewards: 272.9449533704885, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1929, Total reward: 233.9642754172932, Average Rewards: 272.2129709284752, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1930, Total reward: 252.76511031250726, Average Rewards: 271.51052509005257, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1931, Total reward: 261.3611567059095, Average Rewards: 271.6022208236401, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1932, Total reward: 252.2410580324488, Average Rewards: 271.7524892027439, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1933, Total reward: 296.5056591787916, Average Rewards: 271.42724233986166, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1934, Total reward: 252.71972102263473, Average Rewards: 271.97340328238994, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1935, Total reward: 257.0397780162066, Average Rewards: 272.1110123976891, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1936, Total reward: 256.74033856286735, Average Rewards: 271.9509058547077, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1937, Total reward: 268.9422475305947, Average Rewards: 271.6737674569136, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1938, Total reward: 267.8899410063011, Average Rewards: 271.997774298565, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1939, Total reward: 239.4455567541488, Average Rewards: 272.0273101131354, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1940, Total reward: 266.653041321504, Average Rewards: 271.4546337861567, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1941, Total reward: 272.0977444928377, Average Rewards: 271.1175594934561, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1942, Total reward: 270.21440931959137, Average Rewards: 270.9744265083757, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1943, Total reward: 125.99625311699272, Average Rewards: 271.23791755087956, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1944, Total reward: 258.57464049823415, Average Rewards: 269.99775669837857, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1945, Total reward: 243.7028781345788, Average Rewards: 270.0126611831979, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1946, Total reward: 289.3503523938002, Average Rewards: 270.1732711470278, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1947, Total reward: 281.08455071368667, Average Rewards: 270.4124675073233, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1948, Total reward: 302.70409466044896, Average Rewards: 270.58695626215734, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1949, Total reward: 274.853607666479, Average Rewards: 270.7920962474469, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1950, Total reward: 224.93471516802776, Average Rewards: 270.8817685097203, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1951, Total reward: 278.5256599448169, Average Rewards: 270.27009445104267, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1952, Total reward: 299.1759735213626, Average Rewards: 271.55142081860697, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1953, Total reward: 276.1894111956193, Average Rewards: 271.48823364931303, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1954, Total reward: 247.86693802070482, Average Rewards: 271.4758435859751, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1955, Total reward: 248.5477033496883, Average Rewards: 271.3676658323239, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1956, Total reward: 288.34348780177174, Average Rewards: 271.39702552392185, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1957, Total reward: 230.6173909959947, Average Rewards: 271.5191928938978, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1958, Total reward: 272.6380257612933, Average Rewards: 271.30385885040283, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1959, Total reward: 298.649746838531, Average Rewards: 271.05424970895143, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1960, Total reward: 265.56534011817973, Average Rewards: 271.561634759456, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1961, Total reward: 232.85647955685945, Average Rewards: 271.38695300911763, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1962, Total reward: 272.4940413607659, Average Rewards: 271.42695801797095, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1963, Total reward: 288.36372665605666, Average Rewards: 271.6126798632799, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1964, Total reward: 275.11090303271214, Average Rewards: 271.87919330993395, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1965, Total reward: 289.63331302625784, Average Rewards: 271.9150908807315, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1966, Total reward: 253.5268710354755, Average Rewards: 272.0808976842279, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1967, Total reward: 283.6824548343938, Average Rewards: 271.4869642477923, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1968, Total reward: 258.8831986274844, Average Rewards: 271.88997406418946, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1969, Total reward: 259.7566728493, Average Rewards: 271.4762169216891, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1970, Total reward: 235.5577908203762, Average Rewards: 271.35695827434085, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1971, Total reward: 260.1304731126422, Average Rewards: 270.907736291648, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1972, Total reward: 260.73950441932334, Average Rewards: 270.85815803336993, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1973, Total reward: 271.4692329643234, Average Rewards: 270.5671695083876, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1974, Total reward: 150.68744248651973, Average Rewards: 270.781988131003, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1975, Total reward: 289.1940936349696, Average Rewards: 269.4962449588345, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1976, Total reward: 264.619187154474, Average Rewards: 269.7442464865035, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1977, Total reward: 257.1619116049884, Average Rewards: 269.6854876128353, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1978, Total reward: 249.75673897723706, Average Rewards: 269.74254799581485, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1979, Total reward: 275.35063767338283, Average Rewards: 269.3512491361859, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1980, Total reward: 266.6027536462289, Average Rewards: 269.50784223679875, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1981, Total reward: 278.06932872942605, Average Rewards: 269.27423570722544, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1982, Total reward: 259.3812330516195, Average Rewards: 269.2512278683742, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1983, Total reward: 275.2779663388154, Average Rewards: 269.0856518826963, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1984, Total reward: 242.4160015658378, Average Rewards: 268.8324938133625, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1985, Total reward: 251.1988504317368, Average Rewards: 268.5831138511938, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1986, Total reward: 305.34384846172253, Average Rewards: 268.13430499716594, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1987, Total reward: 279.29888293223166, Average Rewards: 268.1781720674119, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1988, Total reward: 276.38870060979, Average Rewards: 268.0890831718828, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1989, Total reward: 295.58597519007, Average Rewards: 268.1574841034342, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1990, Total reward: 263.9549108917606, Average Rewards: 268.4765571913078, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1991, Total reward: 302.13752612287595, Average Rewards: 268.2921905246701, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1992, Total reward: 264.0605982006623, Average Rewards: 268.16434955461733, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1993, Total reward: 257.54264372347046, Average Rewards: 267.8256052090302, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1994, Total reward: 267.743792800092, Average Rewards: 267.24309629365365, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1995, Total reward: 272.1031358438985, Average Rewards: 267.02517734869247, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1996, Total reward: 304.07751106675147, Average Rewards: 266.8958653754356, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1997, Total reward: 275.8875956798146, Average Rewards: 267.362894281168, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1998, Total reward: 250.94056615104785, Average Rewards: 267.5108096825043, Epsilon: 0.01, Learning_rate: 0.0003\n",
      "Episode: 1999, Total reward: 279.1024814063198, Average Rewards: 267.3991971077572, Epsilon: 0.01, Learning_rate: 0.0003\n"
     ]
    }
   ],
   "source": [
    "gamma = 0.99\n",
    "batch_size = 64\n",
    "lr = 0.0003\n",
    "t_interval_update = 6\n",
    "memory_size = 150000\n",
    "epsilon_start = 1.0\n",
    "epsilon_final = 0.01\n",
    "epsilon_decay = 150000\n",
    "\n",
    "\n",
    "env = gym.make('LunarLander-v2')\n",
    "env.seed(RANDOM_SEED)\n",
    "input_size = env.observation_space.shape[0]\n",
    "output_size = env.action_space.n\n",
    "\n",
    "\n",
    "\n",
    "num_episodes = 2000\n",
    "\n",
    "rolling_window = 100\n",
    "episode_rewards = []\n",
    "avg_rewards = []\n",
    "\n",
    "def train():\n",
    "    agent = Agent()\n",
    "    episode_rewards = []\n",
    "    epsilon = epsilon_start\n",
    "    \n",
    "    for episode in range(num_episodes):\n",
    "        state = env.reset()\n",
    "        total_reward = 0\n",
    "        done = False\n",
    "\n",
    "        while not done:\n",
    "            action = agent.get_action(state, epsilon)\n",
    "            next_state, reward, done, _ = env.step(action)\n",
    "            agent.replay_buffer.add(state, action, reward, next_state, done)\n",
    "            state = next_state\n",
    "            total_reward += reward\n",
    "\n",
    "            if len(agent.replay_buffer) > batch_size:\n",
    "                agent.q_updation(gamma, batch_size)\n",
    "\n",
    "            epsilon = max(epsilon_final, epsilon - (epsilon_start - epsilon_final) / epsilon_decay)  # Epsilon decay\n",
    "\n",
    "        episode_rewards.append(total_reward)\n",
    "\n",
    "        if episode % t_interval_update == 0:\n",
    "            agent.target_network.load_state_dict(agent.q_network.state_dict())\n",
    "\n",
    "        if episode >= rolling_window:\n",
    "            avg_reward = np.mean(episode_rewards[episode-rolling_window:episode])\n",
    "            avg_rewards.append(avg_reward)\n",
    "        else:\n",
    "            avg_reward = np.mean(episode_rewards[:episode+1])\n",
    "            avg_rewards.append(avg_reward)\n",
    "        \n",
    "        current_lr = agent.optimizer.param_groups[0]['lr']\n",
    "        print('Episode: {}, Total reward: {}, Average Rewards: {}, Epsilon: {:.2f}, Learning_rate: {}'.format(episode, total_reward, avg_reward, epsilon, current_lr))\n",
    "\n",
    "    return episode_rewards, avg_rewards\n",
    "\n",
    "    plt.plot(episode_rewards)\n",
    "    plt.title('Episode Rewards')\n",
    "    plt.xlabel('Episode')\n",
    "    plt.ylabel('Reward')\n",
    "    plt.show()\n",
    "    \n",
    "    return episode_rewards, avg_rewards\n",
    "\n",
    "    \n",
    "episode_rewards, avg_rewards = train()\n",
    "env.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 618
    },
    "id": "GxFdy52tmf3G",
    "outputId": "e00d16c9-02b5-4845-febd-a00fb38b26fc"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, ax = plt.subplots(figsize=(12, 6))\n",
    "\n",
    "ax.plot(avg_rewards, label='Average of last 100 episodes')\n",
    "ax.plot(episode_rewards, alpha=0.5, label='Episode Reward')\n",
    "\n",
    "ax.set_xlabel('Episodes')\n",
    "ax.set_ylabel('Reward')\n",
    "ax.set_title('Rewards and Moving Average Rewards per Episode')\n",
    "ax.legend()\n",
    "ax.grid(True)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "# print('avg_rewards', avg_rewards)\n",
    "# print('episode_rewards', episode_rewards)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "s2rricAktigM"
   },
   "outputs": [],
   "source": [
    "def save_agent(agent, filepath):\n",
    "    \n",
    "    os.makedirs(os.path.dirname(filepath), exist_ok=True)\n",
    "    torch.save({\n",
    "        'q_network_state_dict': agent.q_network.state_dict(),\n",
    "        'target_network_state_dict': agent.target_network.state_dict(),\n",
    "        'optimizer_state_dict': agent.optimizer.state_dict()\n",
    "    }, filepath)\n",
    "\n",
    "\n",
    "def load_agent(agent, filepath):\n",
    "    checkpoint = torch.load(filepath)\n",
    "\n",
    "    agent.q_network.load_state_dict(checkpoint['q_network_state_dict'])\n",
    "    agent.target_network.load_state_dict(checkpoint['target_network_state_dict'])\n",
    "    agent.optimizer.load_state_dict(checkpoint['optimizer_state_dict'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_agent(agent, 'Saved_files/DQN/dqn_agent.pth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "episode_rewards = np.array(episode_rewards)\n",
    "episode_rewards.dump(open('Saved_files/DQN/DQN_episode_rewards.npy', 'wb'))\n",
    "\n",
    "avg_rewards = np.array(avg_rewards)\n",
    "avg_rewards.dump(open('Saved_files/DQN/DQN_avg_rewards.npy', 'wb'))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#To render the environment with agents\n",
    "import time\n",
    "def play_episodes(agent, env_name='LunarLander-v2', num_episodes=10, render=True, save_dir):\n",
    "    env = gym.make(env_name)\n",
    "    env = RecordVideo(env, save_dir)\n",
    "    total_rewards = []\n",
    "\n",
    "    for episode in range(num_episodes):\n",
    "        state = env.reset()\n",
    "        episode_reward = 0\n",
    "        done = False\n",
    "\n",
    "        while not done:\n",
    "            if render:\n",
    "                env.render()\n",
    "                time.sleep(0.01)  # Slow down the rendering for better visualization\n",
    "\n",
    "            state_tensor = torch.FloatTensor(state).unsqueeze(0).to(device)\n",
    "            with torch.no_grad():\n",
    "                action = agent.q_network(state_tensor).max(1)[1].item()\n",
    "            \n",
    "            next_state, reward, done, _ = env.step(action)\n",
    "            state = next_state\n",
    "            episode_reward += reward\n",
    "\n",
    "        total_rewards.append(episode_reward)\n",
    "        print(f'Episode {episode + 1}: Total Reward: {episode_reward}')\n",
    "\n",
    "    env.close()\n",
    "    avg_reward = np.mean(total_rewards)\n",
    "    print(f'Average Reward over {num_episodes} episodes: {avg_reward}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Play Random Agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_agent = Agent()\n",
    "play_episodes(random_agent, './Saved_files/DQN')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Play DQN Trained Agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "trained_agent = load_agent(agent, './Saved_files/DQN/dqn_agent.pth')\n",
    "play_episodes(trained_agent, './Saved_files/DQN')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}