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

好的，下面是代码：

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

# 定义神经网络
class Net(nn.Module):
    def __init__(self, n_states, n_actions):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(n_states, 64)
        self.fc2 = nn.Linear(64, 64)
        self.out = nn.Linear(64, n_actions)

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

# 定义经验回放缓存
class ReplayBuffer():
    def __init__(self, buffer_size):
        self.buffer = deque(maxlen=buffer_size)

    def add(self, experience):
        self.buffer.append(experience)

    def sample(self, batch_size):
        batch = random.sample(self.buffer, batch_size)
        state_batch = torch.cat([torch.tensor([exp[0]]) for exp in batch])
        action_batch = torch.tensor([exp[1] for exp in batch])
        reward_batch = torch.tensor([exp[2] for exp in batch])
        next_state_batch = torch.cat([torch.tensor([exp[3]]) for exp in batch])
        done_batch = torch.tensor([exp[4] for exp in batch])
        return state_batch, action_batch, reward_batch, next_state_batch, done_batch

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

# 定义DDQN算法
class DDQNAgent():
    def __init__(self, env):
        self.env = env
        self.n_states = env.observation_space.shape[0]
        self.n_actions = env.action_space.n
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.policy_net = Net(self.n_states, self.n_actions).to(self.device)
        self.target_net = Net(self.n_states, self.n_actions).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(), lr=0.001)
        self.memory = ReplayBuffer(10000)
        self.batch_size = 64
        self.gamma = 0.99
        self.epsilon = 1.0
        self.epsilon_min = 0.01
        self.epsilon_decay = 0.995
        self.steps = 0
        self.update_freq = 1000

    def select_action(self, state):
        if random.random() < self.epsilon:
            return self.env.action_space.sample()
        else:
            with torch.no_grad():
                state = torch.tensor(state, dtype=torch.float).to(self.device)
                q_values = self.policy_net(state)
                action = q_values.argmax().item()
                return action

    def train(self):
        if len(self.memory) < self.batch_size:
            return
        state_batch, action_batch, reward_batch, next_state_batch, done_batch = self.memory.sample(self.batch_size)
        q_values = self.policy_net(state_batch).gather(1, action_batch.unsqueeze(1)).squeeze(1)
        next_q_values = self.target_net(next_state_batch).max(1)[0]
        expected_q_values = reward_batch + (1 - done_batch) * self.gamma * next_q_values
        loss = F.mse_loss(q_values, expected_q_values)
        self.optimizer.zero_grad()
        loss.backward()
        self.optimizer.step()
        self.steps += 1
        if self.steps % self.update_freq == 0:
            self.target_net.load_state_dict(self.policy_net.state_dict())

    def run(self, episodes):
        for episode in range(episodes):
            state = self.env.reset()
            total_reward = 0
            done = False
            while not done:
                action = self.select_action(state)
                next_state, reward, done, _ = self.env.step(action)
                total_reward += reward
                self.memory.add((state, action, reward, next_state, done))
                self.train()
                state = next_state
                if done:
                    break
            print("Episode: {}, Total reward: {}, Epsilon: {:.2f}".format(episode, total_reward, self.epsilon))
            if self.epsilon > self.epsilon_min:
                self.epsilon *= self.epsilon_decay

# 运行代码
env = gym.make("MountainCar-v0")
agent = DDQNAgent(env)
agent.run(1000)

注意：这段代码需要使用pytorch和gym库，并且由于MountainCar环境比较简单，所以只需要运行1000个episode就可以得到比较好的结果。如果需要在更复杂的环境中使用DDQN进行强化学习，可能需要更多的episode和更大的经验回放缓存。