deepq_network.py

import torch
import torch.nn as nn
import torch.nn.functional as F

class CNN(nn.Module):

    def __init__(self, env_actions):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, kernel_size=8, stride=4)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=4, stride=2)
        self.conv3 = nn.Conv2d(64, 64, kernel_size=3, stride=1)
        self.fc    = nn.Linear(3136, 512) # 64 x 7 x 7
        self.out   = nn.Linear(512, env_actions)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = F.relu(self.conv2(x))
        x = F.relu(self.conv3(x))
        x = self.conv_to_fc(x)
        x = F.relu(self.fc(x))

        return self.out(x)

    def conv_to_fc(self, x):
        size = x.size()[1:] # all dimensions except batch dimension
        num_features = 1
        for s in size:
            num_features *= s

        return x.view(-1, num_features)


class LinearMapNet(nn.Module):
    def __init__(self, input_shape, env_actions):
        super(LinearMapNet, self).__init__()
        self.fc1 = nn.Linear(input_shape, 64)
        self.fc2 = nn.Linear(64, 64)
        self.out = nn.Linear(64, env_actions)

    def forward(self, x):
        x = torch.tanh(self.fc1(x))
        x = torch.tanh(self.fc2(x))

        return self.out(x)