请给我一个FrozenLake作为测试的基于pytorch的强化学习模型

FrozenLake是一个经典的强化学习环境，由OpenAI Gym库提供，它是一个二维网格世界，在其中有一个冰湖和几个可以行走的格子。目标是安全地从起始位置走到终点，同时避开滑倒掉进湖里。为了在这个环境中使用PyTorch构建一个强化学习模型，你可以选择Q-learning、策略梯度方法或其他深度学习算法，如DQN（Deep Q-Networks）。

下面是一个简单的基于PyTorch的DQN模型的示例：

import gym
from collections import deque
import torch
import torch.nn as nn
import torch.optim as optim

class DQN(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(DQN, self).__init__()
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.fc2 = nn.Linear(hidden_size, hidden_size)
        self.fc3 = nn.Linear(hidden_size, output_size)

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

# 初始化环境
env = gym.make('FrozenLake-v0')

# 定义超参数
input_size = env.observation_space.n
hidden_size = 256
output_size = env.action_space.n
batch_size = 32
gamma = 0.99  # 学习率衰减因子
epsilon = 1.0  # 初始探索概率
eps_decay = 0.995  # 探索概率随时间递减
max_episodes = 10000
memory_capacity = 10000

model = DQN(input_size, hidden_size, output_size)
optimizer = optim.Adam(model.parameters())
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

# 训练过程
for episode in range(max_episodes):
    state = env.reset()
    state = torch.tensor([state], dtype=torch.float).to(device)
    
    done = False
    total_reward = 0
    
    while not done:
        with torch.no_grad():
            action = model(state).max(1)[1].item()
            
        # 混合随机策略和探索策略
        if torch.rand(1) < epsilon:
            action = env.action_space.sample()

        next_state, reward, done, _ = env.step(action)
        next_state = torch.tensor([next_state], dtype=torch.float).to(device)
        
        memory.push(state, action, reward, next_state, done)
        
        total_reward += reward
        
        if len(memory) > batch_size:
            experiences = memory.sample(batch_size)
            states, actions, rewards, next_states, dones = zip(*experiences)

            states = torch.cat(states)
            actions = torch.tensor(actions)
            rewards = torch.tensor(rewards, dtype=torch.float)
            next_states = torch.cat(next_states)
            dones = torch.tensor(dones)

            q_values = model(states).gather(1, actions.unsqueeze(1)).squeeze(1)
            next_q_values = model(next_states).detach().max(1)[0]
            expected_q_values = rewards + gamma * (1 - dones) * next_q_values
            
            loss = criterion(q_values, expected_q_values)
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
            
        state = next_state
        epsilon *= eps_decay

print(f"Episode {episode+1}: Total Reward = {total_reward}")

别忘了安装必要的库，例如`gym`、`torch`等，并根据需要调整参数。训练完成后，你可以观察模型在 FrozenLake 环境中的性能并评估其学习效果。