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dqn_model_y.py
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dqn_model_y.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import autograd
class DQN(nn.Module):
def __init__(self, device, h=84, w=84, outputs=4):
super(DQN, self).__init__()
self.conv1 = nn.Conv2d(4, 64, kernel_size=8, stride=4, bias=False)
self.conv2 = nn.Conv2d(64, 128, kernel_size=4, stride=2, bias=False)
self.conv3 = nn.Conv2d(128, 128, kernel_size=3, stride=1, bias=False)
self.fc1 = nn.Linear(128 * 7 * 7, 1024)
self.fc2 = nn.Linear(1024, outputs)
self.device = device
self.to(device)
def init_weights(self, m):
if type(m) == nn.Linear:
torch.nn.init.kaiming_normal_(m.weight, nonlinearity='relu')
m.bias.data.fill_(0.0)
if type(m) == nn.Conv2d:
torch.nn.init.kaiming_normal_(m.weight, nonlinearity='relu')
# m.bias.data.fill_(0.1)
def forward(self, x):
#print(x.shape)
#x = x.permute(0, 3, 1, 2).contiguous()
#x = x.to(self.device).float() / 255.
x = F.relu(self.conv1(x))
x = F.relu(self.conv2(x))
x = F.relu(self.conv3(x))
x = x.view(x.size(0), -1)
x = F.relu(self.fc1(x))
return self.fc2(x)
def save(self, path):
torch.save(self.state_dict(), path)