python强化学习工具包调度

强化学习是一种通过智能体与环境的交互来学习最优行为的机器学习方法。在强化学习中，调度问题是一个重要的应用场景。Python中有很多强化学习工具包可以用来解决调度问题，其中比较流行的是PyTorch和TensorFlow等框架。

在PyTorch中，可以使用Actor-Critic算法来解决调度问题。Actor-Critic算法是一种基于策略梯度的强化学习算法，它通过同时学习一个策略函数和一个值函数来提高学习效率。在调度问题中，策略函数可以用来生成作业的调度序列，值函数可以用来评估每个调度序列的质量。

下面是一个使用Actor-Critic算法解决作业车间调度（JSP）问题的示例代码：

import torch
import torch.nn as nn
import torch.optim as optim
import gym

# 定义Actor和Critic网络
class Actor(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(Actor, self).__init__()
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.fc2 = nn.Linear(hidden_size, output_size)
        self.softmax = nn.Softmax(dim=1)
    def forward(self, x):
        x = torch.relu(self.fc1(x))
        x = self.softmax(self.fc2(x))
        return x

class Critic(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(Critic, self).__init__()
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.fc2 = nn.Linear(hidden_size, output_size)

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

# 定义Actor-Critic算法
class ActorCritic:
    def __init__(self, env, gamma=0.99, lr=0.01):
        self.env = env
        self.gamma = gamma
        self.lr = lr
        self.actor = Actor(env.observation_space.shape[0], 128, env.action_space.n)
        self.critic = Critic(env.observation_space.shape[0], 128, 1)
        self.optimizer_actor = optim.Adam(self.actor.parameters(), lr=self.lr)
        self.optimizer_critic = optim.Adam(self.critic.parameters(), lr=self.lr)

    def select_action(self, state):
        state = torch.from_numpy(state).float().unsqueeze(0)
        probs = self.actor(state)
        m = torch.distributions.Categorical(probs)
        action = m.sample()
        return action.item()

    def update(self, rewards, log_probs, values):
        R = 0
        returns = []
        for r in reversed(rewards):
            R = r + self.gamma * R
            returns.insert(0, R)
        returns = torch.tensor(returns)
        log_probs = torch.stack(log_probs)
        values = torch.stack(values).squeeze()

        advantage = returns - values
        actor_loss = -(log_probs * advantage.detach()).mean()
        critic_loss = advantage.pow(2).mean()

        self.optimizer_actor.zero_grad()
        self.optimizer_critic.zero_grad()
        actor_loss.backward()
        critic_loss.backward()
        self.optimizer_actor.step()
        self.optimizer_critic.step()

    def train(self, num_episodes):
        for i in range(num_episodes):
            state = self.env.reset()
            done = False
            rewards = []
            log_probs = []
            values = []
            while not done:
                action = self.select_action(state)
                next_state, reward, done, _ = self.env.step(action)
                log_prob = torch.log(self.actor(torch.from_numpy(state).float().unsqueeze(0))[0][action])
                value = self.critic(torch.from_numpy(state).float().unsqueeze(0))
                rewards.append(reward)
                log_probs.append(log_prob)
                values.append(value)
                state = next_state
            self.update(rewards, log_probs, values)

# 使用Actor-Critic算法解决作业车间调度问题
env = gym.make('JSP-v0')
ac = ActorCritic(env)
ac.train(1000)
optimal_policy = ac.actor(torch.from_numpy(env.reset()).float().unsqueeze(0))
print("Optimal policy:", optimal_policy.argmax(dim=1))