dqn路径规划 python

DQN（Deep Q-Network）是一种深度强化学习算法，可以用于路径规划问题。在Python中，你可以使用一些开源的深度强化学习库来实现DQN算法，如TensorFlow或PyTorch。

下面是一个使用PyTorch实现DQN路径规划的简单示例代码：

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

# 定义Q网络
class QNetwork(nn.Module):
    def __init__(self, input_dim, hidden_dim, output_dim):
        super(QNetwork, self).__init__()
        self.fc1 = nn.Linear(input_dim, hidden_dim)
        self.fc2 = nn.Linear(hidden_dim, output_dim)
        
    def forward(self, x):
        x = torch.relu(self.fc1(x))
        x = self.fc2(x)
        return x

# 定义经验回放缓冲区
class ReplayBuffer():
    def __init__(self, capacity):
        self.capacity = capacity
        self.buffer = []
        
    def push(self, transition):
        if len(self.buffer) >= self.capacity:
            self.buffer.pop(0)
        self.buffer.append(transition)
        
    def sample(self, batch_size):
        indices = np.random.choice(len(self.buffer), batch_size, replace=False)
        states, actions, rewards, next_states, dones = zip(*[self.buffer[i] for i in indices])
        return np.array(states), np.array(actions), np.array(rewards), np.array(next_states), np.array(dones)

# 定义DQN Agent
class DQNAgent():
    def __init__(self, input_dim, hidden_dim, output_dim, lr, capacity, batch_size, gamma):
        self.q_network = QNetwork(input_dim, hidden_dim, output_dim)
        self.target_network = QNetwork(input_dim, hidden_dim, output_dim)
        self.target_network.load_state_dict(self.q_network.state_dict())
        self.optimizer = optim.Adam(self.q_network.parameters(), lr=lr)
        self.replay_buffer = ReplayBuffer(capacity)
        self.batch_size = batch_size
        self.gamma = gamma
        
    def select_action(self, state, epsilon):
        if np.random.uniform() < epsilon:
            return np.random.choice(self.q_network.out_features)
        else:
            with torch.no_grad():
                q_values = self.q_network(torch.tensor(state, dtype=torch.float32))
                return torch.argmax(q_values).item()
        
    def train(self):
        states, actions, rewards, next_states, dones = self.replay_buffer.sample(self.batch_size)
        
        q_values = self.q_network(torch.tensor(states, dtype=torch.float32))
        next_q_values = self.target_network(torch.tensor(next_states, dtype=torch.float32)).detach()
        
        targets = rewards + self.gamma * torch.max(next_q_values, dim=1)[0] * (1 - dones)
        
        loss = nn.MSELoss()(q_values.gather(1, torch.tensor(actions[:, None])) , targets.unsqueeze(1))
        
        self.optimizer.zero_grad()
        loss.backward()
        self.optimizer.step()
        
    def update_target_network(self):
        self.target_network.load_state_dict(self.q_network.state_dict())

# 创建DQN Agent并训练
input_dim = ...  # 输入维度
hidden_dim = ...  # 隐层维度
output_dim = ...  # 输出维度
lr = ...  # 学习率
capacity = ...  # 经验回放缓冲区容量
batch_size = ...  # 批次大小
gamma = ...  # 折扣因子

agent = DQNAgent(input_dim, hidden_dim, output_dim, lr, capacity, batch_size, gamma)

# 使用agent与环境交互，更新经验回放缓冲区和训练网络

# 相关问题: