dqn 路径规划 pytorch

您可以参考以下步骤来实现 DQN 路径规划：

1. 定义状态空间和动作空间，例如，状态空间可以是地图上的位置，动作空间可以是向上、向下、向左、向右等。
2. 构建神经网络模型，用于估计每个动作的 Q 值。
3. 初始化经验回放缓存区，用于存储智能体的经验。
4. 在每个时间步中，根据当前状态选择一个动作，并执行该动作。同时，将当前状态、动作、奖励、下一个状态存储到经验回放缓存区中。
5. 从经验回放缓存区中随机采样一批经验，用于训练神经网络模型。
6. 更新神经网络模型的参数，使其能够更好地预测每个动作的 Q 值。
7. 重复步骤 4-6，直到达到预定的训练次数或者智能体已经学会了最优策略。

关于 DQN 路径规划的 PyTorch 实现，您可以参考以下代码：

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

class DQN(nn.Module):
    def __init__(self, state_dim, action_dim, hidden_dim):
        super(DQN, self).__init__()
        self.fc1 = nn.Linear(state_dim, hidden_dim)
        self.fc2 = nn.Linear(hidden_dim, hidden_dim)
        self.fc3 = nn.Linear(hidden_dim, action_dim)

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

class ReplayBuffer:
    def __init__(self, capacity):
        self.capacity = capacity
        self.buffer = []
        self.position = 0

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

    def sample(self, batch_size):
        batch = np.random.choice(self.buffer, batch_size, replace=False)
        state, action, reward, next_state = zip(*batch)
        return state, action, reward, next_state

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

def train(model, optimizer, replay_buffer, batch_size, gamma):
    if len(replay_buffer) < batch_size:
        return
    state, action, reward, next_state = replay_buffer.sample(batch_size)
    state = torch.FloatTensor(state)
    action = torch.LongTensor(action)
    reward = torch.FloatTensor(reward)
    next_state = torch.FloatTensor(next_state)

    q_values = model(state)
    next_q_values = model(next_state).detach()
    max_next_q_values = torch.max(next_q_values, dim=1)[0]
    target_q_values = reward + gamma * max_next_q_values

    loss = nn.functional.mse_loss(q_values.gather(1, action.unsqueeze(1)), target_q_values.unsqueeze(1))
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

def select_action(model, state, eps):
    if np.random.rand() < eps:
        return np.random.randint(model.fc3.out_features)
    else:
        state = torch.FloatTensor(state)
        q_values = model(state)
        return torch.argmax(q_values).item()

def dqn_path_planning(env, model, optimizer, replay_buffer, batch_size, gamma, eps_start, eps_end, eps_decay, num_episodes):
    eps = eps_start
    for i in range(num_episodes):
        state = env.reset()
        done = False
        total_reward = 0
        while not done:
            action = select_action(model, state, eps)
            next_state, reward, done, _ = env.step(action)
            replay_buffer.push(state, action, reward, next_state)
            train(model, optimizer, replay_buffer, batch_size, gamma)
            state = next_state
            total_reward += reward
        eps = max(eps_end, eps_decay * eps)
        print("Episode {}: Total Reward = {}".format(i + 1, total_reward))

# 定义环境
env = ...

# 定义超参数
state_dim = ...
action_dim = ...
hidden_dim = ...
capacity = ...
batch_size = ...
gamma = ...
eps_start = ...
eps_end = ...
eps_decay = ...
num_episodes = ...

# 初始化神经网络模型、优化器和经验回放缓存区
model = DQN(state_dim, action_dim, hidden_dim)
optimizer = optim.Adam(model.parameters())
replay_buffer = ReplayBuffer(capacity)

# 训练神经网络模型
dqn_path_planning(env, model, optimizer, replay_buffer, batch_size, gamma, eps_start, eps_end, eps_decay, num_episodes)