具体怎么用代码实现用DQN完成BipedalWalkerHardcore_v3环境

使用DQN算法来解决BipedalWalkerHardcore_v3环境需要进行以下步骤：

1. 安装 gym 和 pytorch 库

pip install gym torch

2. 定义神经网络模型

在这里我们使用一个简单的三层全连接神经网络作为模型。

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

class DQN(nn.Module):
    def __init__(self, input_shape, num_actions):
        super(DQN, self).__init__()
        self.fc1 = nn.Linear(input_shape, 128)
        self.fc2 = nn.Linear(128, 128)
        self.fc3 = nn.Linear(128, num_actions)

    def forward(self, x):
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

3. 定义DQN算法

我们使用经典的DQN算法来训练模型。DQN算法中使用了一个经验回放缓存区来存储历史经验，然后从缓存区中随机采样一批数据进行训练。

import random
import numpy as np
import torch.optim as optim
import torch.nn.functional as F

class DQNAgent:
    def __init__(self, state_shape, num_actions, learning_rate, gamma, epsilon_start, epsilon_end, epsilon_decay):
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.state_shape = state_shape
        self.num_actions = num_actions
        self.learning_rate = learning_rate
        self.gamma = gamma
        self.epsilon = epsilon_start
        self.epsilon_end = epsilon_end
        self.epsilon_decay = epsilon_decay
        self.model = DQN(state_shape, num_actions).to(self.device)
        self.target_model = DQN(state_shape, num_actions).to(self.device)
        self.optimizer = optim.Adam(self.model.parameters(), lr=learning_rate)
        self.memory = []

    def act(self, state):
        if random.random() < self.epsilon:
            return random.randint(0, self.num_actions - 1)
        else:
            state = torch.FloatTensor(state).unsqueeze(0).to(self