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custom_policy.py
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import torch as th
import torch.nn as nn
from gymnasium import spaces
from stable_baselines3.common.torch_layers import BaseFeaturesExtractor
import numpy as np
import torch.nn.functional as F
from sb3_contrib.common.maskable.policies import MaskableActorCriticPolicy
from stable_baselines3.common.policies import ActorCriticPolicy
from stable_baselines3.common.policies import ContinuousCritic
from typing import Callable, Dict, List, Optional, Tuple, Type, Union
#Shared
class NoChangeExtractor(BaseFeaturesExtractor):
def __init__(self, observation_space: spaces.Box, features_dim: int = 256):
super().__init__(spaces.Box, features_dim)
def forward(self, observations: th.Tensor) -> th.Tensor:
return observations
#MaskablePPO version
class CustomActorCriticPolicy(MaskableActorCriticPolicy):
def __init__(
self,
observation_space: spaces.Space,
action_space: spaces.Space,
lr_schedule: Callable[[float], float],
*args,
**kwargs,
):
# Disable orthogonal initialization
kwargs["ortho_init"] = False
super().__init__(
observation_space,
action_space,
lr_schedule,
# Pass remaining arguments to base class
*args,
**kwargs,
)
def _build_mlp_extractor(self) -> None:
self.mlp_extractor = CustomNetwork(self.features_dim)
#PPO/A2C version
# class CustomActorCriticPolicy(ActorCriticPolicy):
# def __init__(
# self,
# observation_space: spaces.Space,
# action_space: spaces.Space,
# lr_schedule: Callable[[float], float],
# *args,
# **kwargs,
# ):
# # Disable orthogonal initialization
# kwargs["ortho_init"] = False
# super().__init__(
# observation_space,
# action_space,
# lr_schedule,
# # Pass remaining arguments to base class
# *args,
# **kwargs,
# )
# def _build_mlp_extractor(self) -> None:
# self.mlp_extractor = CustomNetwork(self.features_dim)
#Network #1
# class CustomNetwork(nn.Module):
# def __init__(
# self,
# feature_dim: int,
# last_layer_dim_pi: int = 4,
# last_layer_dim_vf: int = 4,
# ):
# super().__init__()
# self.latent_dim_pi = last_layer_dim_pi
# self.latent_dim_vf = last_layer_dim_vf
# self.pi1 = nn.Conv2d(1, 32, kernel_size=(3,3), stride=1, padding=1,padding_mode='zeros')
# self.pi2 = nn.Conv2d(32, 64, kernel_size=(3,3), stride=1, padding=1,padding_mode='zeros')
# self.pi3 = nn.Conv2d(64, 128, kernel_size=(3,3), stride=1, padding=1,padding_mode='zeros')
# self.pi4 = nn.Linear(2048, last_layer_dim_pi)
# self.vf1 = nn.Conv2d(1, 32, kernel_size=(3,3), stride=1, padding=1,padding_mode='zeros')
# self.vf2 = nn.Conv2d(32, 64, kernel_size=(3,3), stride=1, padding=1,padding_mode='zeros')
# self.vf3 = nn.Conv2d(64, 128, kernel_size=(3,3), stride=1, padding=1,padding_mode='zeros')
# self.vf4 = nn.Linear(2048, last_layer_dim_vf)
# def forward(self, features: th.Tensor) -> Tuple[th.Tensor, th.Tensor]:
# """
# :return: (th.Tensor, th.Tensor) latent_policy, latent_value of the specified network.
# If all layers are shared, then ``latent_policy == latent_value``
# """
# return self.forward_actor(features), self.forward_critic(features)
# def forward_actor(self, features: th.Tensor) -> th.Tensor:
# x = features
# x = th.unsqueeze(x,1)
# x = F.relu(self.pi1(x))
# x = F.max_pool2d(x,kernel_size=(2,2))
# x = F.relu(self.pi2(x))
# x = F.max_pool2d(x,kernel_size=(2,2))
# x = F.relu(self.pi3(x))
# x = th.flatten(x,start_dim=1)
# x = F.relu(self.pi4(x))
# return x
# def forward_critic(self, features: th.Tensor) -> th.Tensor:
# x = features
# x = th.unsqueeze(x,1)
# x = F.relu(self.vf1(x))
# x = F.max_pool2d(x,kernel_size=(2,2))
# x = F.relu(self.vf2(x))
# x = F.max_pool2d(x,kernel_size=(2,2))
# x = F.relu(self.vf3(x))
# x = th.flatten(x,start_dim=1)
# x = F.relu(self.vf4(x))
# return x
#Network #2
# class CustomNetwork(nn.Module):
# def __init__(
# self,
# feature_dim: int,
# last_layer_dim_pi: int = 8,
# last_layer_dim_vf: int = 8,
# ):
# super().__init__()
# self.latent_dim_pi = last_layer_dim_pi
# self.latent_dim_vf = last_layer_dim_vf
# self.pi1 = nn.Conv2d(1, 32, kernel_size=(5,5), stride=1, padding=2,padding_mode='zeros')
# self.pi2 = nn.Conv2d(32, 64, kernel_size=(5,5), stride=1, padding=2,padding_mode='zeros')
# self.pi3 = nn.Conv2d(64, 128, kernel_size=(5,5), stride=1, padding=2,padding_mode='zeros')
# self.pi4 = nn.Linear(2048, last_layer_dim_pi)
# self.vf1 = nn.Conv2d(1, 32, kernel_size=(5,5), stride=1, padding=2,padding_mode='zeros')
# self.vf2 = nn.Conv2d(32, 64, kernel_size=(5,5), stride=1, padding=2,padding_mode='zeros')
# self.vf3 = nn.Conv2d(64, 128, kernel_size=(5,5), stride=1, padding=2,padding_mode='zeros')
# self.vf4 = nn.Linear(2048, last_layer_dim_vf)
# def forward(self, features: th.Tensor) -> Tuple[th.Tensor, th.Tensor]:
# """
# :return: (th.Tensor, th.Tensor) latent_policy, latent_value of the specified network.
# If all layers are shared, then ``latent_policy == latent_value``
# """
# return self.forward_actor(features), self.forward_critic(features)
# def forward_actor(self, features: th.Tensor) -> th.Tensor:
# x = features
# x = th.unsqueeze(x,1)
# x = F.relu(self.pi1(x))
# x = F.max_pool2d(x,kernel_size=(2,2))
# x = F.relu(self.pi2(x))
# x = F.max_pool2d(x,kernel_size=(2,2))
# x = F.relu(self.pi3(x))
# x = th.flatten(x,start_dim=1)
# x = F.relu(self.pi4(x))
# return x
# def forward_critic(self, features: th.Tensor) -> th.Tensor:
# x = features
# x = th.unsqueeze(x,1)
# x = F.relu(self.vf1(x))
# x = F.max_pool2d(x,kernel_size=(2,2))
# x = F.relu(self.vf2(x))
# x = F.max_pool2d(x,kernel_size=(2,2))
# x = F.relu(self.vf3(x))
# x = th.flatten(x,start_dim=1)
# x = F.relu(self.vf4(x))
# return x
#Network #3 (without maxpool2d)
# class CustomNetwork(nn.Module):
# def __init__(
# self,
# feature_dim: int,
# last_layer_dim_pi: int = 8,
# last_layer_dim_vf: int = 8,
# ):
# super().__init__()
# self.latent_dim_pi = last_layer_dim_pi
# self.latent_dim_vf = last_layer_dim_vf
# self.pi1 = nn.Conv2d(1, 32, kernel_size=(5,5), stride=1, padding=2,padding_mode='zeros')
# self.pi2 = nn.Conv2d(32, 64, kernel_size=(5,5), stride=1, padding=2,padding_mode='zeros')
# self.pi3 = nn.Conv2d(64, 128, kernel_size=(5,5), stride=1, padding=2,padding_mode='zeros')
# self.pi4 = nn.Linear(32768, last_layer_dim_pi)
# self.vf1 = nn.Conv2d(1, 32, kernel_size=(5,5), stride=1, padding=2,padding_mode='zeros')
# self.vf2 = nn.Conv2d(32, 64, kernel_size=(5,5), stride=1, padding=2,padding_mode='zeros')
# self.vf3 = nn.Conv2d(64, 128, kernel_size=(5,5), stride=1, padding=2,padding_mode='zeros')
# self.vf4 = nn.Linear(32768, last_layer_dim_vf)
# def forward(self, features: th.Tensor) -> Tuple[th.Tensor, th.Tensor]:
# """
# :return: (th.Tensor, th.Tensor) latent_policy, latent_value of the specified network.
# If all layers are shared, then ``latent_policy == latent_value``
# """
# return self.forward_actor(features), self.forward_critic(features)
# def forward_actor(self, features: th.Tensor) -> th.Tensor:
# x = features
# x = th.unsqueeze(x,1)
# x = F.relu(self.pi1(x))
# x = F.relu(self.pi2(x))
# x = F.relu(self.pi3(x))
# x = th.flatten(x,start_dim=1)
# x = F.relu(self.pi4(x))
# return x
# def forward_critic(self, features: th.Tensor) -> th.Tensor:
# x = features
# x = th.unsqueeze(x,1)
# x = F.relu(self.vf1(x))
# x = F.relu(self.vf2(x))
# x = F.relu(self.vf3(x))
# x = th.flatten(x,start_dim=1)
# x = F.relu(self.vf4(x))
# return x
#Network #4 (without relu)
# class CustomNetwork(nn.Module):
# def __init__(
# self,
# feature_dim: int,
# last_layer_dim_pi: int = 8,
# last_layer_dim_vf: int = 8,
# ):
# super().__init__()
# self.latent_dim_pi = last_layer_dim_pi
# self.latent_dim_vf = last_layer_dim_vf
# self.pi1 = nn.Conv2d(1, 32, kernel_size=(5,5), stride=1, padding=2,padding_mode='zeros')
# self.pi2 = nn.Conv2d(32, 64, kernel_size=(5,5), stride=1, padding=2,padding_mode='zeros')
# self.pi3 = nn.Conv2d(64, 128, kernel_size=(5,5), stride=1, padding=2,padding_mode='zeros')
# self.pi4 = nn.Linear(2048, last_layer_dim_pi)
# self.vf1 = nn.Conv2d(1, 32, kernel_size=(5,5), stride=1, padding=2,padding_mode='zeros')
# self.vf2 = nn.Conv2d(32, 64, kernel_size=(5,5), stride=1, padding=2,padding_mode='zeros')
# self.vf3 = nn.Conv2d(64, 128, kernel_size=(5,5), stride=1, padding=2,padding_mode='zeros')
# self.vf4 = nn.Linear(2048, last_layer_dim_vf)
# def forward(self, features: th.Tensor) -> Tuple[th.Tensor, th.Tensor]:
# """
# :return: (th.Tensor, th.Tensor) latent_policy, latent_value of the specified network.
# If all layers are shared, then ``latent_policy == latent_value``
# """
# return self.forward_actor(features), self.forward_critic(features)
# def forward_actor(self, features: th.Tensor) -> th.Tensor:
# x = features
# x = th.unsqueeze(x,1)
# x = self.pi1(x)
# x = F.max_pool2d(x,kernel_size=(2,2))
# x = self.pi2(x)
# x = F.max_pool2d(x,kernel_size=(2,2))
# x = self.pi3(x)
# x = th.flatten(x,start_dim=1)
# x = self.pi4(x)
# return x
# def forward_critic(self, features: th.Tensor) -> th.Tensor:
# x = features
# x = th.unsqueeze(x,1)
# x = self.vf1(x)
# x = F.max_pool2d(x,kernel_size=(2,2))
# x = self.vf2(x)
# x = F.max_pool2d(x,kernel_size=(2,2))
# x = self.vf3(x)
# x = th.flatten(x,start_dim=1)
# x = self.vf4(x)
# return x
#Network #5 (without maxpool2d, 1 channel to 256 channels to 1 chanel)
# class CustomNetwork(nn.Module):
# def __init__(
# self,
# feature_dim: int,
# last_layer_dim_pi: int = 8,
# last_layer_dim_vf: int = 8,
# ):
# super().__init__()
# self.latent_dim_pi = last_layer_dim_pi
# self.latent_dim_vf = last_layer_dim_vf
# self.pi1 = nn.Conv2d(1, 32, kernel_size=(5,5), stride=1, padding=2,padding_mode='zeros')
# self.pi2 = nn.Conv2d(32, 64, kernel_size=(5,5), stride=1, padding=2,padding_mode='zeros')
# self.pi3 = nn.Conv2d(64, 128, kernel_size=(5,5), stride=1, padding=2,padding_mode='zeros')
# self.pi4 = nn.Conv2d(128, 256, kernel_size=(5,5), stride=1, padding=2,padding_mode='zeros')
# self.pi5 = nn.Conv2d(256, 128, kernel_size=(5,5), stride=1, padding=2,padding_mode='zeros')
# self.pi6 = nn.Conv2d(128, 64, kernel_size=(5,5), stride=1, padding=2,padding_mode='zeros')
# self.pi7 = nn.Conv2d(64, 32, kernel_size=(5,5), stride=1, padding=2,padding_mode='zeros')
# self.pi8 = nn.Conv2d(32, 1, kernel_size=(5,5), stride=1, padding=2,padding_mode='zeros')
# self.pi9 = nn.Linear(256, last_layer_dim_pi)
# self.vf1 = nn.Conv2d(1, 32, kernel_size=(5,5), stride=1, padding=2,padding_mode='zeros')
# self.vf2 = nn.Conv2d(32, 64, kernel_size=(5,5), stride=1, padding=2,padding_mode='zeros')
# self.vf3 = nn.Conv2d(64, 128, kernel_size=(5,5), stride=1, padding=2,padding_mode='zeros')
# self.vf4 = nn.Conv2d(128, 256, kernel_size=(5,5), stride=1, padding=2,padding_mode='zeros')
# self.vf5 = nn.Conv2d(256, 128, kernel_size=(5,5), stride=1, padding=2,padding_mode='zeros')
# self.vf6 = nn.Conv2d(128, 64, kernel_size=(5,5), stride=1, padding=2,padding_mode='zeros')
# self.vf7 = nn.Conv2d(64, 32, kernel_size=(5,5), stride=1, padding=2,padding_mode='zeros')
# self.vf8 = nn.Conv2d(32, 1, kernel_size=(5,5), stride=1, padding=2,padding_mode='zeros')
# self.vf9 = nn.Linear(256, last_layer_dim_pi)
# def forward(self, features: th.Tensor) -> Tuple[th.Tensor, th.Tensor]:
# """
# :return: (th.Tensor, th.Tensor) latent_policy, latent_value of the specified network.
# If all layers are shared, then ``latent_policy == latent_value``
# """
# return self.forward_actor(features), self.forward_critic(features)
# def forward_actor(self, features: th.Tensor) -> th.Tensor:
# x = features
# x = th.unsqueeze(x,1)
# x = F.relu(self.pi1(x))
# x = F.relu(self.pi2(x))
# x = F.relu(self.pi3(x))
# x = F.relu(self.pi4(x))
# x = F.relu(self.pi5(x))
# x = F.relu(self.pi6(x))
# x = F.relu(self.pi7(x))
# x = F.relu(self.pi8(x))
# x = th.flatten(x,start_dim=1)
# x = F.relu(self.pi9(x))
# return x
# def forward_critic(self, features: th.Tensor) -> th.Tensor:
# x = features
# x = th.unsqueeze(x,1)
# x = F.relu(self.vf1(x))
# x = F.relu(self.vf2(x))
# x = F.relu(self.vf3(x))
# x = F.relu(self.vf4(x))
# x = F.relu(self.vf5(x))
# x = F.relu(self.vf6(x))
# x = F.relu(self.vf7(x))
# x = F.relu(self.vf8(x))
# x = th.flatten(x,start_dim=1)
# x = F.relu(self.vf9(x))
# return x
#Network 6 (without maxpool, 1 channel to 256 channels)
# class CustomNetwork(nn.Module):
# def __init__(
# self,
# feature_dim: int,
# last_layer_dim_pi: int = 8,
# last_layer_dim_vf: int = 8,
# ):
# super().__init__()
# self.latent_dim_pi = last_layer_dim_pi
# self.latent_dim_vf = last_layer_dim_vf
# self.pi1 = nn.Conv2d(1, 16, kernel_size=(5,5), stride=1, padding=2,padding_mode='zeros')
# self.pi2 = nn.Conv2d(16, 32, kernel_size=(5,5), stride=1, padding=2,padding_mode='zeros')
# self.pi3 = nn.Conv2d(32, 64, kernel_size=(5,5), stride=1, padding=2,padding_mode='zeros')
# self.pi4 = nn.Conv2d(64, 128, kernel_size=(5,5), stride=1, padding=2,padding_mode='zeros')
# self.pi5 = nn.Conv2d(128, 256, kernel_size=(5,5), stride=1, padding=2,padding_mode='zeros')
# self.pi6 = nn.Linear(65536, last_layer_dim_pi)
# self.vf1 = nn.Conv2d(1, 16, kernel_size=(5,5), stride=1, padding=2,padding_mode='zeros')
# self.vf2 = nn.Conv2d(16, 32, kernel_size=(5,5), stride=1, padding=2,padding_mode='zeros')
# self.vf3 = nn.Conv2d(32, 64, kernel_size=(5,5), stride=1, padding=2,padding_mode='zeros')
# self.vf4 = nn.Conv2d(64, 128, kernel_size=(5,5), stride=1, padding=2,padding_mode='zeros')
# self.vf5 = nn.Conv2d(128, 256, kernel_size=(5,5), stride=1, padding=2,padding_mode='zeros')
# self.vf6 = nn.Linear(65536, last_layer_dim_pi)
# def forward(self, features: th.Tensor) -> Tuple[th.Tensor, th.Tensor]:
# """
# :return: (th.Tensor, th.Tensor) latent_policy, latent_value of the specified network.
# If all layers are shared, then ``latent_policy == latent_value``
# """
# return self.forward_actor(features), self.forward_critic(features)
# def forward_actor(self, features: th.Tensor) -> th.Tensor:
# x = features
# x = th.unsqueeze(x,1)
# x = F.relu(self.pi1(x))
# x = F.relu(self.pi2(x))
# x = F.relu(self.pi3(x))
# x = F.relu(self.pi4(x))
# x = F.relu(self.pi5(x))
# x = th.flatten(x,start_dim=1)
# x = F.relu(self.pi6(x))
# return x
# def forward_critic(self, features: th.Tensor) -> th.Tensor:
# x = features
# x = th.unsqueeze(x,1)
# x = F.relu(self.vf1(x))
# x = F.relu(self.vf2(x))
# x = F.relu(self.vf3(x))
# x = F.relu(self.vf4(x))
# x = F.relu(self.vf5(x))
# x = th.flatten(x,start_dim=1)
# x = F.relu(self.vf6(x))
# return x
#Network 7 (no maxpool, less layers)
# class CustomNetwork(nn.Module):
# def __init__(
# self,
# feature_dim: int,
# last_layer_dim_pi: int = 8,
# last_layer_dim_vf: int = 8,
# ):
# super().__init__()
# self.latent_dim_pi = last_layer_dim_pi
# self.latent_dim_vf = last_layer_dim_vf
# self.pi1 = nn.Conv2d(1, 32, kernel_size=(5,5), stride=1, padding=2,padding_mode='zeros')
# self.pi2 = nn.Linear(8192, last_layer_dim_pi)
# self.vf1 = nn.Conv2d(1, 32, kernel_size=(5,5), stride=1, padding=2,padding_mode='zeros')
# self.vf2 = nn.Linear(8192, last_layer_dim_pi)
# def forward(self, features: th.Tensor) -> Tuple[th.Tensor, th.Tensor]:
# """
# :return: (th.Tensor, th.Tensor) latent_policy, latent_value of the specified network.
# If all layers are shared, then ``latent_policy == latent_value``
# """
# return self.forward_actor(features), self.forward_critic(features)
# def forward_actor(self, features: th.Tensor) -> th.Tensor:
# x = features
# x = th.unsqueeze(x,1)
# x = F.relu(self.pi1(x))
# x = th.flatten(x,start_dim=1)
# x = F.relu(self.pi2(x))
# return x
# def forward_critic(self, features: th.Tensor) -> th.Tensor:
# x = features
# x = th.unsqueeze(x,1)
# x = F.relu(self.vf1(x))
# x = th.flatten(x,start_dim=1)
# x = F.relu(self.vf2(x))
# return x
#Network 8 (maxpool, conv kernel=7x7)
# class CustomNetwork(nn.Module):
# def __init__(
# self,
# feature_dim: int,
# last_layer_dim_pi: int = 8,
# last_layer_dim_vf: int = 8,
# ):
# super().__init__()
# self.latent_dim_pi = last_layer_dim_pi
# self.latent_dim_vf = last_layer_dim_vf
# self.pi1 = nn.Conv2d(1, 32, kernel_size=(7,7), stride=1, padding=3,padding_mode='zeros')
# self.pi2 = nn.Conv2d(32, 64, kernel_size=(7,7), stride=1, padding=3,padding_mode='zeros')
# self.pi3 = nn.Conv2d(64, 128, kernel_size=(7,7), stride=1, padding=3,padding_mode='zeros')
# self.pi4 = nn.Linear(2048, last_layer_dim_pi)
# self.vf1 = nn.Conv2d(1, 32, kernel_size=(7,7), stride=1, padding=3,padding_mode='zeros')
# self.vf2 = nn.Conv2d(32, 64, kernel_size=(7,7), stride=1, padding=3,padding_mode='zeros')
# self.vf3 = nn.Conv2d(64, 128, kernel_size=(7,7), stride=1, padding=3,padding_mode='zeros')
# self.vf4 = nn.Linear(2048, last_layer_dim_vf)
# def forward(self, features: th.Tensor) -> Tuple[th.Tensor, th.Tensor]:
# """
# :return: (th.Tensor, th.Tensor) latent_policy, latent_value of the specified network.
# If all layers are shared, then ``latent_policy == latent_value``
# """
# return self.forward_actor(features), self.forward_critic(features)
# def forward_actor(self, features: th.Tensor) -> th.Tensor:
# x = features
# x = th.unsqueeze(x,1)
# x = F.relu(self.pi1(x))
# x = F.max_pool2d(x,kernel_size=(2,2))
# x = F.relu(self.pi2(x))
# x = F.max_pool2d(x,kernel_size=(2,2))
# x = F.relu(self.pi3(x))
# x = th.flatten(x,start_dim=1)
# x = F.relu(self.pi4(x))
# return x
# def forward_critic(self, features: th.Tensor) -> th.Tensor:
# x = features
# x = th.unsqueeze(x,1)
# x = F.relu(self.vf1(x))
# x = F.max_pool2d(x,kernel_size=(2,2))
# x = F.relu(self.vf2(x))
# x = F.max_pool2d(x,kernel_size=(2,2))
# x = F.relu(self.vf3(x))
# x = th.flatten(x,start_dim=1)
# x = F.relu(self.vf4(x))
# return x
#Network 9 (no maxpool, kernel 7x7)
class CustomNetwork(nn.Module):
def __init__(
self,
feature_dim: int,
last_layer_dim_pi: int = 8,
last_layer_dim_vf: int = 8,
):
super().__init__()
self.latent_dim_pi = last_layer_dim_pi
self.latent_dim_vf = last_layer_dim_vf
self.pi1 = nn.Conv2d(1, 32, kernel_size=(7,7), stride=1, padding=3,padding_mode='zeros')
self.pi2 = nn.Conv2d(32, 64, kernel_size=(7,7), stride=1, padding=3,padding_mode='zeros')
self.pi3 = nn.Conv2d(64, 128, kernel_size=(7,7), stride=1, padding=3,padding_mode='zeros')
self.pi4 = nn.Linear(32768, last_layer_dim_pi)
self.vf1 = nn.Conv2d(1, 32, kernel_size=(7,7), stride=1, padding=3,padding_mode='zeros')
self.vf2 = nn.Conv2d(32, 64, kernel_size=(7,7), stride=1, padding=3,padding_mode='zeros')
self.vf3 = nn.Conv2d(64, 128, kernel_size=(7,7), stride=1, padding=3,padding_mode='zeros')
self.vf4 = nn.Linear(32768, last_layer_dim_vf)
def forward(self, features: th.Tensor) -> Tuple[th.Tensor, th.Tensor]:
"""
:return: (th.Tensor, th.Tensor) latent_policy, latent_value of the specified network.
If all layers are shared, then ``latent_policy == latent_value``
"""
return self.forward_actor(features), self.forward_critic(features)
def forward_actor(self, features: th.Tensor) -> th.Tensor:
x = features
x = th.unsqueeze(x,1)
x = F.relu(self.pi1(x))
x = F.relu(self.pi2(x))
x = F.relu(self.pi3(x))
x = th.flatten(x,start_dim=1)
x = F.relu(self.pi4(x))
return x
def forward_critic(self, features: th.Tensor) -> th.Tensor:
x = features
x = th.unsqueeze(x,1)
x = F.relu(self.vf1(x))
x = F.relu(self.vf2(x))
x = F.relu(self.vf3(x))
x = th.flatten(x,start_dim=1)
x = F.relu(self.vf4(x))
return x
#https://stable-baselines3.readthedocs.io/en/master/guide/custom_policy.html#default-network-architecture