强化学习作业

• 本次实验使用了Dueling DQN来优化了，下面主要更改是utils_model.py中神经网络的结构，以下是更改后代码

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


class Dueling_DQN(nn.Module):

    def __init__(self, action_dim, device):
        super(Dueling_DQN, self).__init__()
        self.num_actions = action_dim

        self.__conv1 = nn.Conv2d(4, 32, kernel_size=8, stride=4, bias=False)
        self.__conv2 = nn.Conv2d(32, 64, kernel_size=4, stride=2, bias=False)
        self.__conv3 = nn.Conv2d(64, 64, kernel_size=3, stride=1, bias=False)
        
        # 两个全链层，一个用来生成value，一个用来生成advantage
        self.__fc1_adv = nn.Linear(64*7*7, 512)
        self.__fc1_val = nn.Linear(64*7*7, 512)

        self.__fc2_adv = nn.Linear(512, action_dim)
        self.__fc2_val = nn.Linear(512, 1)
        self.__device = device

    def forward(self, x):
        x = x / 255.
        x = F.relu(self.__conv1(x))
        x = F.relu(self.__conv2(x))
        x = F.relu(self.__conv3(x))
        
        # 得到advantage值和value值
        adv = F.relu(self.__fc1_adv(x.view(x.size(0), -1)))
        adv = self.__fc2_adv(adv)
        
        val = F.relu(self.__fc1_val(x.view(x.size(0), -1)))
        val = self.__fc2_val(val).expand(x.size(0), self.num_actions)
        
        # 综合两种结果来确定当前的Q值，并返回
        res = val + adv - adv.mean(1).unsqueeze(1).expand(x.size(0), self.num_actions)
        return res

    @staticmethod
    def init_weights(module):
        if isinstance(module, nn.Linear):
            torch.nn.init.kaiming_normal_(module.weight, nonlinearity="relu")
            module.bias.data.fill_(0.0)
        elif isinstance(module, nn.Conv2d):
            torch.nn.init.kaiming_normal_(module.weight, nonlinearity="relu")