models.py

import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torchvision.transforms as T

class DQNbn(nn.Module):
    def __init__(self, in_channels=4, n_actions=14):
        """
        Initialize Deep Q Network

        Args:
            in_channels (int): number of input channels
            n_actions (int): number of outputs
        """
        super(DQNbn, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, 32, kernel_size=8, stride=4)
        self.bn1 = nn.BatchNorm2d(32)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=4, stride=2)
        self.bn2 = nn.BatchNorm2d(64)
        self.conv3 = nn.Conv2d(64, 64, kernel_size=3, stride=1)
        self.bn3 = nn.BatchNorm2d(64)
        self.fc4 = nn.Linear(7 * 7 * 64, 512)
        self.head = nn.Linear(512, n_actions)
        
    def forward(self, x):
        x = x.float() / 255
        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 = F.relu(self.fc4(x.view(x.size(0), -1)))
        return self.head(x)


class DQN(nn.Module):
    def __init__(self, in_channels=4, n_actions=14):
        """
        Initialize Deep Q Network

        Args:
            in_channels (int): number of input channels
            n_actions (int): number of outputs
        """
        super(DQN, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, 32, kernel_size=8, stride=4)
        # self.bn1 = nn.BatchNorm2d(32)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=4, stride=2)
        # self.bn2 = nn.BatchNorm2d(64)
        self.conv3 = nn.Conv2d(64, 64, kernel_size=3, stride=1)
        # self.bn3 = nn.BatchNorm2d(64)
        self.fc4 = nn.Linear(7 * 7 * 64, 512)
        self.head = nn.Linear(512, n_actions)
        
    def forward(self, x):
        x = x.float() / 255
        x = F.relu(self.conv1(x))
        x = F.relu(self.conv2(x))
        x = F.relu(self.conv3(x))
        x = F.relu(self.fc4(x.view(x.size(0), -1)))
        return self.head(x)