qn.py

import math
import matplotlib.pyplot as plt
import tensorflow as tf
import tensorflow_datasets as tfds

from tensorflow import keras
from keras import layers
import os
#os.environ['CUDA_VISIBLE_DEVICES'] = '-1'

import random
import gym
import numpy as np
from collections import deque
from keras.models import Model, load_model
from keras.layers import Input, Dense
from keras.optimizers import Adam, RMSprop


def OurModel(input_shape, action_space):
    X_input = Input(input_shape)
    X = Dense(128, input_shape=input_shape, activation="relu", kernel_initializer='he_uniform')(X_input)
    X = Dense(64, activation="relu", kernel_initializer='he_uniform')(X)
    X = Dense(action_space, activation="linear", kernel_initializer='he_uniform')(X)
    model = Model(inputs=X_input, outputs=X)
    model.compile(loss="mse", optimizer=Adam(lr=0.001))
    model.summary()
    return model

class DQNAgent:
    def __init__(self):
        self.env = gym.make('CartPole-v1')
        # by default, CartPole-v1 has max episode steps = 500
        self.state_size = self.env.observation_space.shape[0]
        self.action_size = self.env.action_space.n
        self.EPISODES = 1000
        self.memory = deque(maxlen=1024 * 4)

        self.gamma = 0.95  # discount rate
        self.epsilon = 1.0  # exploration rate
        self.epsilon_min = 0.03
        self.epsilon_decay = 0.999
        self.batch_size = 1024
        self.epoch_count = 4
        self.train_start = 1024
        self.visualize = True

        # create main model
        self.model = OurModel(input_shape=(self.state_size,), action_space=self.action_size)

    def remember(self, state, action, reward, next_state, done):
        self.memory.append((state, action, reward, next_state, done))
        if len(self.memory) >= self.train_start:
            if self.epsilon >= self.epsilon_min:
                self.epsilon *= self.epsilon_decay

    def act(self, state):
        if np.random.random() <= self.epsilon:
            return random.randrange(self.action_size)
        else:
            return np.argmax(self.model.predict(state, verbose=0))

    def replay(self):
        if len(self.memory) < self.train_start:
            return
        # Randomly sample minibatch from the memory
        minibatch = random.sample(self.memory, min(len(self.memory), self.batch_size))

        state = np.zeros((self.batch_size, self.state_size))
        next_state = np.zeros((self.batch_size, self.state_size))
        action, reward, done = [], [], []

        # do this before prediction
        # for speedup, this could be done on the tensor level
        # but easier to understand using a loop
        for i in range(self.batch_size):
            state[i] = minibatch[i][0]
            action.append(minibatch[i][1])
            reward.append(minibatch[i][2])
            next_state[i] = minibatch[i][3]
            done.append(minibatch[i][4])

        # do batch prediction to save speed
        target = self.model.predict(state, verbose=0)
        target_next = self.model.predict(next_state, verbose=0)

        for i in range(self.batch_size):
            # correction on the Q value for the action used
            if done[i]:
                target[i][action[i]] = reward[i]
            else:
                # Standard - DQN
                # DQN chooses the max Q value among next actions
                # selection and evaluation of action is on the target Q Network
                # Q_max = max_a' Q_target(s', a')
                target[i][action[i]] = reward[i] + self.gamma * (np.amax(target_next[i]))

        # Train the Neural Network with batches
        self.model.fit(state, target, batch_size=self.batch_size, epochs=self.epoch_count, verbose=0)


    def load(self, name):
        self.model = load_model(name)

    def save(self, name):
        self.model.save(name)

    def run(self):
        for e in range(self.EPISODES):
            state = self.env.reset()
            state = np.reshape(state, [1, self.state_size])
            done = False
            i = 0
            while not done:
                if self.visualize:
                    self.env.render()
                action = self.act(state)
                next_state, reward, done, _ = self.env.step(action)
                next_state = np.reshape(next_state, [1, self.state_size])
                if not done or i == self.env._max_episode_steps - 1:
                    reward = reward
                else:
                    reward = -100
                self.remember(state, action, reward, next_state, done)
                state = next_state
                i += 1
                if done:
                    print("episode: {}/{}, score: {}, e: {:.2}".format(e, self.EPISODES, i, self.epsilon))
                    print("Saving trained model as cartpole-dqn.h5")
                    self.save("cartpole-dqn2.h5")
                    #return
            self.replay()

    def test(self):
        self.load("cartpole-dqn.h5")
        for e in range(self.EPISODES):
            state = self.env.reset()
            state = np.reshape(state, [1, self.state_size])
            done = False
            i = 0
            while not done:
                self.env.render()
                action = np.argmax(self.model.predict(state, verbose=0))
                next_state, reward, done, _ = self.env.step(action)
                state = np.reshape(next_state, [1, self.state_size])
                i += 1
                if done:
                    print("episode: {}/{}, score: {}".format(e, self.EPISODES, i))
                    break


if __name__ == "__main__":
    agent = DQNAgent()
    #agent.model.load_weights('cartpole-dqn2.h5')
    #agent.epsilon_min = 0.0
    #agent.epsilon = agent.epsilon_min
    #agent.visualize = True
    #agent.run()
    # agent.test()