dqn路径规划pytorch

DQN（Deep Q-Network）是一种基于深度学习的增强学习算法，可以用于路径规划任务。在使用PyTorch实现DQN路径规划时，你可以按照以下步骤进行：

1. 导入必要的库：

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

2. 定义Q网络：

class QNetwork(nn.Module):
    def __init__(self, state_size, action_size):
        super(QNetwork, self).__init__()
        self.fc1 = nn.Linear(state_size, 64)
        self.fc2 = nn.Linear(64, 64)
        self.fc3 = nn.Linear(64, action_size)

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

3. 定义DQN Agent：

class DQNAgent:
    def __init__(self, state_size, action_size):
        self.state_size = state_size
        self.action_size = action_size
        self.q_network = QNetwork(state_size, action_size)
        self.optimizer = optim.Adam(self.q_network.parameters(), lr=0.001)

    def get_action(self, state, epsilon):
        if np.random.rand() <= epsilon:
            return np.random.choice(self.action_size)
        else:
            state = torch.from_numpy(state).float().unsqueeze(0)
            q_values = self.q_network(state)
            return torch.argmax(q_values).item()

    def train(self, state, action, reward, next_state, done):
        state = torch.from_numpy(state).float().unsqueeze(0)
        next_state = torch.from_numpy(next_state).float().unsqueeze(0)
        action = torch.tensor(action).unsqueeze(0)
        reward = torch.tensor(reward).unsqueeze(0)
        done = torch.tensor(done).unsqueeze(0)

        q_values = self.q_network(state)
        next_q_values = self.q_network(next_state)
        target = reward + (1 - done) * torch.max(next_q_values, dim=1)[0]
        target = target.detach()

        loss = nn.MSELoss()(q_values.gather(1, action.unsqueeze(1)), target.unsqueeze(1))

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

4. 定义训练循环：

env = YourEnvironment()  # 替换为你的环境类
state_size = env.state_size
action_size = env.action_size
agent = DQNAgent(state_size, action_size)

episodes = 1000
epsilon = 1.0
epsilon_decay = 0.999
epsilon_min = 0.01
batch_size = 32

for episode in range(episodes):
    state = env.reset()
    total_reward = 0

    while True:
        action = agent.get_action(state, epsilon)
        next_state, reward, done = env.step(action)
        agent.train(state, action, reward, next_state, done)
        state = next_state
        total_reward += reward

        if done:
            break

    epsilon *= epsilon_decay
    epsilon = max(epsilon, epsilon_min)

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

这是一个简单的使用PyTorch实现DQN路径规划的示例，具体的代码需要根据你的环境和任务需求进行调整。希望对你有所帮助！