DQN pytorch代码

以下是一个使用PyTorch实现的DQN算法的代码示例：

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

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

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

# 定义经验回放缓冲区
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 = random.sample(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算法
class DQNAgent():
    def __init__(self, state_size, action_size, batch_size, gamma, epsilon, epsilon_decay, epsilon_min, learning_rate, target_update):
        self.state_size = state_size
        self.action_size = action_size
        self.batch_size = batch_size
        self.gamma = gamma
        self.epsilon = epsilon
        self.epsilon_decay = epsilon_decay
        self.epsilon_min = epsilon_min
        self.learning_rate = learning_rate
        self.target_update = target_update

        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

        self.policy_net = DQN(state_size, action_size).to(self.device)
        self.target_net = DQN(state_size, action_size).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=learning_rate)
        self.memory = ReplayBuffer(10000)
        self.steps_done = 0

    def select_action(self, state):
        if random.random() > self.epsilon:
            with torch.no_grad():
                state = torch.tensor(state, dtype=torch.float32).unsqueeze(0).to(self.device)
                q_values = self.policy_net(state)
                action = q_values.max(1)[1].item()
        else:
            action = random.randrange(self.action_size)
        self.steps_done += 1
        self.epsilon = max(self.epsilon_min, self.epsilon * self.epsilon_decay)
        return action

    def update_model(self):
        if len(self.memory) < self.batch_size:
            return
        states, actions, rewards, next_states, dones = self.memory.sample(self.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.policy_net(states).gather(1, actions)
        next_q_values = self.target_net(next_states).max(1)[0].unsqueeze(1)
        expected_q_values = rewards + self.gamma * next_q_values * (1 - dones)

        loss = F.smooth_l1_loss(q_values, expected_q_values)

        self.optimizer.zero_grad()
        loss.backward()
        self.optimizer.step()

        if self.steps_done % self.target_update == 0:
            self.target_net.load_state_dict(self.policy_net.state_dict())

# 创建DQNAgent实例
state_size = 4
action_size = 2
batch_size = 32
gamma = 0.99
epsilon = 1.0
epsilon_decay = 0.995
epsilon_min = 0.01
learning_rate = 0.001
target_update = 100

agent = DQNAgent(state_size, action_size, batch_size, gamma, epsilon, epsilon_decay, epsilon_min, learning_rate, target_update)

# 训练DQN
for episode in range(1000):
    state = env.reset()
    done = False
    total_reward = 0

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

        agent.update_model()

    if episode % 100 == 0:
        print("Episode: {}, Total Reward: {}".format(episode, total_reward))

# 使用训练好的DQN进行预测
state = env.reset()
done = False
while not done:
    action = agent.select_action(state)
    state, reward, done, _ = env.step(action)
    env.render()