dqn pytorch代码

DQN（Deep Q-Network）是一种基于深度学习的强化学习算法，用于解决离散动作空间的问题。下面是一个简单的DQN PyTorch代码的介绍：

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np

# 定义DQN网络
class DQN(nn.Module):
    def __init__(self, input_dim, output_dim):
        super(DQN, self).__init__()
        self.fc1 = nn.Linear(input_dim, 64)
        self.fc2 = nn.Linear(64, 64)
        self.fc3 = nn.Linear(64, output_dim)

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

# 定义经验回放缓存
class ReplayBuffer():
    def __init__(self, capacity):
        self.capacity = capacity
        self.buffer = []
        self.position = 0

    def push(self, state, action, reward, next_state, done):
        if len(self.buffer) < self.capacity:
            self.buffer.append(None)
        self.buffer[self.position] = (state, action, reward, next_state, done)
        self.position = (self.position + 1) % self.capacity

    def sample(self, batch_size):
        batch = np.random.choice(self.buffer, batch_size)
        states, actions, rewards, next_states, dones = zip(*batch)
        return np.array(states), np.array(actions), np.array(rewards), np.array(next_states), np.array(dones)

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

# 定义DQN Agent
class DQNAgent():
    def __init__(self, input_dim, output_dim, lr, gamma, epsilon):
        self.input_dim = input_dim
        self.output_dim = output_dim
        self.lr = lr
        self.gamma = gamma
        self.epsilon = epsilon
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

        self.model = DQN(input_dim, output_dim).to(self.device)
        self.target_model = DQN(input_dim, output_dim).to(self.device)
        self.target_model.load_state_dict(self.model.state_dict())
        self.target_model.eval()

        self.optimizer = optim.Adam(self.model.parameters(), lr=self.lr)
        self.loss_fn = nn.MSELoss()

        self.replay_buffer = ReplayBuffer(capacity=10000)

    def select_action(self, state):
        if np.random.rand() < self.epsilon:
            return np.random.randint(self.output_dim)
        state = torch.tensor(state, dtype=torch.float32).unsqueeze(0).to(self.device)
        q_values = self.model(state)
        return torch.argmax(q_values).item()

    def train(self, batch_size):
        if len(self.replay_buffer) < batch_size:
            return

        states, actions, rewards, next_states, dones = self.replay_buffer.sample(batch_size)
        states = torch.tensor(states, dtype=torch.float32).to(self.device)
        actions = torch.tensor(actions, dtype=torch.long).unsqueeze(1).to(self.device)
        rewards = torch.tensor(rewards, dtype=torch.float32).unsqueeze(1).to(self.device)
        next_states = torch.tensor(next_states, dtype=torch.float32).to(self.device)
        dones = torch.tensor(dones, dtype=torch.float32).unsqueeze(1).to(self.device)

        q_values = self.model(states).gather(1, actions)
        next_q_values = self.target_model(next_states).max(1)[0].unsqueeze(1)
        target_q_values = rewards + self.gamma * next_q_values * (1 - dones)

        loss = self.loss_fn(q_values, target_q_values.detach())
        self.optimizer.zero_grad()
        loss.backward()
        self.optimizer.step()

    def update_target_model(self):
        self.target_model.load_state_dict(self.model.state_dict())

    def store_experience(self, state, action, reward, next_state, done):
        self.replay_buffer.push(state, action, reward, next_state, done)

# 创建DQN Agent实例
input_dim = 4  # 输入维度
output_dim = 2  # 输出维度
lr = 0.001  # 学习率
gamma = 0.99  # 折扣因子
epsilon = 0.1  # 探索率

agent = DQNAgent(input_dim, output_dim, lr, gamma, epsilon)

# 训练DQN Agent
num_episodes = 1000
batch_size = 32

for episode in range(num_episodes):
    state = env.reset()
    done = False
    total_reward = 0

    while not done:
        action = agent.select_action(state)
        next_state, reward, done, _ = env.step(action)
        agent.store_experience(state, action, reward, next_state, done)
        agent.train(batch_size)
        agent.update_target_model()

        state = next_state
        total_reward += reward

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

# 使用训练好的DQN Agent进行预测
state = env.reset()
done = False
total_reward = 0

while not done:
    action = agent.select_action(state)
    next_state, reward, done, _ = env.step(action)

    state = next_state
    total_reward += reward

print(f"Total Reward: {total_reward}")

这段代码实现了一个简单的DQN Agent，包括DQN网络的定义、经验回放缓存的实现、Agent的训练和预测过程。你可以根据自己的需求进行修改和扩展。