import torch
import numpy as np
from torch import nn
import torch.nn.functional as F
[docs]class Actor(nn.Module):
"""For advanced usage (how to customize the network), please refer to
:ref:`build_the_network`.
"""
def __init__(self, preprocess_net, action_shape):
super().__init__()
self.preprocess = preprocess_net
self.last = nn.Linear(128, np.prod(action_shape))
[docs] def forward(self, s, state=None, info={}):
logits, h = self.preprocess(s, state)
logits = F.softmax(self.last(logits), dim=-1)
return logits, h
[docs]class Critic(nn.Module):
"""For advanced usage (how to customize the network), please refer to
:ref:`build_the_network`.
"""
def __init__(self, preprocess_net):
super().__init__()
self.preprocess = preprocess_net
self.last = nn.Linear(128, 1)
[docs] def forward(self, s, **kwargs):
logits, h = self.preprocess(s, state=kwargs.get('state', None))
logits = self.last(logits)
return logits
[docs]class DQN(nn.Module):
"""For advanced usage (how to customize the network), please refer to
:ref:`build_the_network`.
"""
def __init__(self, h, w, action_shape, device='cpu'):
super(DQN, self).__init__()
self.device = device
self.conv1 = nn.Conv2d(4, 16, kernel_size=5, stride=2)
self.bn1 = nn.BatchNorm2d(16)
self.conv2 = nn.Conv2d(16, 32, kernel_size=5, stride=2)
self.bn2 = nn.BatchNorm2d(32)
self.conv3 = nn.Conv2d(32, 32, kernel_size=5, stride=2)
self.bn3 = nn.BatchNorm2d(32)
def conv2d_size_out(size, kernel_size=5, stride=2):
return (size - (kernel_size - 1) - 1) // stride + 1
convw = conv2d_size_out(conv2d_size_out(conv2d_size_out(w)))
convh = conv2d_size_out(conv2d_size_out(conv2d_size_out(h)))
linear_input_size = convw * convh * 32
self.fc = nn.Linear(linear_input_size, 512)
self.head = nn.Linear(512, action_shape)
[docs] def forward(self, x, state=None, info={}):
if not isinstance(x, torch.Tensor):
x = torch.tensor(x, device=self.device, dtype=torch.float32)
x = x.permute(0, 3, 1, 2)
x = F.relu(self.bn1(self.conv1(x)))
x = F.relu(self.bn2(self.conv2(x)))
x = F.relu(self.bn3(self.conv3(x)))
x = self.fc(x.reshape(x.size(0), -1))
return self.head(x), state