请给我用pytorch和DQN实现MountainCar的强化学习的完整代码

好的，以下是使用PyTorch和DQN算法来解决MountainCar问题的完整代码。需要注意的是，这是一个基本的实现，您可以根据需要进行修改和优化。

import gym
import random
import math
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from collections import deque

# 定义神经网络模型
class DQN(nn.Module):
    def __init__(self, num_inputs, num_actions, hidden_size):
        super(DQN, self).__init__()
        self.layers = nn.Sequential(
            nn.Linear(num_inputs, hidden_size),
            nn.ReLU(),
            nn.Linear(hidden_size, hidden_size),
            nn.ReLU(),
            nn.Linear(hidden_size, num_actions)
        )

    def forward(self, x):
        return self.layers(x)

# 定义经验回放缓冲区
class ReplayBuffer(object):
    def __init__(self, capacity):
        self.buffer = deque(maxlen=capacity)

    def push(self, state, action, reward, next_state, done):
        self.buffer.append((state, action, reward, next_state, done))

    def sample(self, batch_size):
        state, action, reward, next_state, done = zip(*random.sample(self.buffer, batch_size))
        return state, action, reward, next_state, done

    def __len__(self):
        return len(self.buffer)

# 定义DQN算法
class DQNAgent(object):
    def __init__(self, env):
        self.env = env
        self.memory = ReplayBuffer(10000)
        self.batch_size = 128
        self.gamma = 0.99
        self.eps_start = 1.0
        self.eps_end = 0.01
        self.eps_decay = 500
        self.target_update = 10
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

        self.policy_net = DQN(env.observation_space.shape[0], env.action_space.n, 128).to(self.device)
        self.target_net = DQN(env.observation_space.shape[0], env.action_space.n, 128).to(self.device)
        self.target_net.load_state_dict(self.policy_net.state_dict())
        self.target_net.eval()

        self.optimizer = optim.Adam(self.policy_net.parameters())
        self.steps_done = 0

    def select_action(self, state):
        eps_threshold = self.eps_end + (self.eps_start - self.eps_end) * \
            math.exp(-1. * self.steps_done / self.eps_decay)
        self.steps_done += 1
        if random.random() > eps_threshold:
            with torch.no_grad():
                state = torch.FloatTensor(state).unsqueeze(0).to(self.device)
                q_value = self.policy_net(state)
                action = q_value.max(1)[1].item()
        else:
            action = self.env.action_space.sample()
        return action

    def optimize_model(self):
        if len(self.memory) < self.batch_size:
            return
        state, action, reward, next_state, done = self.memory.sample(self.batch_size)
        state = torch.FloatTensor(state).to(self.device)
        next_state = torch.FloatTensor(next_state).to(self.device)
        action = torch.LongTensor(action).to(self.device)
        reward = torch.FloatTensor(reward).to(self.device)
        done = torch.FloatTensor(done).to(self.device)

        q_values = self.policy_net(state).gather(1, action.unsqueeze(1)).squeeze(1)
        next_q_values = self.target_net(next_state).max(1)[0]
        expected_q_values = reward + (1 - done) * self.gamma * next_q_values

        loss = F.mse_loss(q_values, expected_q_values.detach())
        self.optimizer.zero_grad()
        loss.backward()
        self.optimizer.step()

    def train(self, num_episodes):
        for i_episode in range(num_episodes):
            state = self.env.reset()
            total_reward = 0
            while True:
                action = self.select_action(state)
                next_state, reward, done, _ = self.env.step(action)
                self.memory.push(state, action, reward, next_state, done)
                state = next_state
                self.optimize_model()
                total_reward += reward
                if done:
                    break
            if i_episode % self.target_update == 0:
                self.target_net.load_state_dict(self.policy_net.state_dict())
            print("Episode: {}, reward: {}".format(i_episode, total_reward))

# 训练模型
env = gym.make("MountainCar-v0")
agent = DQNAgent(env)
agent.train(1000)

希望这个代码对你有所帮助！