怎么关掉pyhon代码中的 gym的log

你可以在代码中添加以下语句来禁用gym的log输出：

import gym
import logging
gym.logger.set_level(logging.CRITICAL)

这将禁用gym的log输出，但仍然保留其他日志信息。如果你想完全禁用所有日志信息，可以使用以下语句：

import gym
import logging
logging.disable(logging.CRITICAL)

这将禁用所有日志输出，包括gym的log。

相关问题

微电网SAC算法python代码

很抱歉，我仍然不能提供完整的微电网SAC算法代码。如之前所述，SAC算法是一种比较复杂的强化学习算法，而微电网是一个特定的应用场景，需要针对具体的问题进行实现。

不过，我可以给你一些关于如何编写微电网SAC算法的Python代码的指导。

下面是一个简单的SAC算法的Python代码，用于离散动作空间：

import gym
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
from collections import deque

class SAC:
    def __init__(self, env, state_dim, action_dim, gamma, alpha, tau):
        self.env = env
        self.state_dim = state_dim
        self.action_dim = action_dim
        self.gamma = gamma
        self.alpha = alpha
        self.tau = tau

        self.actor = Actor(state_dim, action_dim)
        self.critic1 = Critic(state_dim, action_dim)
        self.critic2 = Critic(state_dim, action_dim)
        self.target_critic1 = Critic(state_dim, action_dim)
        self.target_critic2 = Critic(state_dim, action_dim)

        self.actor_optimizer = optim.Adam(self.actor.parameters(), lr=alpha)
        self.critic1_optimizer = optim.Adam(self.critic1.parameters(), lr=alpha)
        self.critic2_optimizer = optim.Adam(self.critic2.parameters(), lr=alpha)

        self.memory = deque(maxlen=100000)
        self.batch_size = 64

    def select_action(self, state):
        state = torch.FloatTensor(state).unsqueeze(0)
        action = self.actor(state).detach().numpy()[0]
        return np.argmax(action)

    def remember(self, state, action, reward, next_state, done):
        self.memory.append((state, action, reward, next_state, done))

    def update(self):
        if len(self.memory) < self.batch_size:
            return

        state, action, reward, next_state, done = zip(*random.sample(self.memory, self.batch_size))

        state = torch.FloatTensor(state)
        action = torch.LongTensor(action).unsqueeze(1)
        reward = torch.FloatTensor(reward).unsqueeze(1)
        next_state = torch.FloatTensor(next_state)
        done = torch.FloatTensor(done).unsqueeze(1)

        target_action, log_prob = self.actor.sample(next_state)
        target_q1 = self.target_critic1(next_state, target_action)
        target_q2 = self.target_critic2(next_state, target_action)
        target_q = torch.min(target_q1, target_q2) - self.alpha * log_prob
        target_q = reward + self.gamma * (1 - done) * target_q.detach()

        q1 = self.critic1(state, action)
        q2 = self.critic2(state, action)
        critic_loss = nn.MSELoss()(q1, target_q) + nn.MSELoss()(q2, target_q)

        self.critic1_optimizer.zero_grad()
        critic_loss.backward()
        self.critic1_optimizer.step()

        self.critic2_optimizer.zero_grad()
        critic_loss.backward()
        self.critic2_optimizer.step()

        policy_loss = (self.alpha * log_prob - self.critic1(state, self.actor(state))).mean()

        self.actor_optimizer.zero_grad()
        policy_loss.backward()
        self.actor_optimizer.step()

        for target_param, param in zip(self.target_critic1.parameters(), self.critic1.parameters()):
            target_param.data.copy_(self.tau * param.data + (1 - self.tau) * target_param.data)

        for target_param, param in zip(self.target_critic2.parameters(), self.critic2.parameters()):
            target_param.data.copy_(self.tau * param.data + (1 - self.tau) * target_param.data)

    def train(self, episodes):
        for i in range(episodes):
            state = self.env.reset()
            done = False
            total_reward = 0

            while not done:
                action = self.select_action(state)
                next_state, reward, done, _ = self.env.step(action)
                self.remember(state, action, reward, next_state, done)
                state = next_state
                total_reward += reward

            self.update()

            print("Episode: {}, Total Reward: {}".format(i, total_reward))

其中，Actor和Critic网络的定义如下：

class Actor(nn.Module):
    def __init__(self, state_dim, action_dim):
        super(Actor, self).__init__()

        self.fc1 = nn.Linear(state_dim, 128)
        self.fc2 = nn.Linear(128, 64)
        self.fc3 = nn.Linear(64, action_dim)

    def forward(self, state):
        x = F.relu(self.fc1(state))
        x = F.relu(self.fc2(x))
        x = F.softmax(self.fc3(x), dim=-1)
        return x

    def sample(self, state):
        probs = self.forward(state)
        dist = Categorical(probs)
        action = dist.sample()
        log_prob = dist.log_prob(action)
        return action, log_prob

class Critic(nn.Module):
    def __init__(self, state_dim, action_dim):
        super(Critic, self).__init__()

        self.fc1 = nn.Linear(state_dim + action_dim, 128)
        self.fc2 = nn.Linear(128, 64)
        self.fc3 = nn.Linear(64, 1)

    def forward(self, state, action):
        x = torch.cat([state, action], dim=-1)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

这段代码仅供参考，实际上，你需要根据微电网的具体问题进行相应的修改。希望这些指导能够帮助你编写微电网SAC算法的Python代码。

写一段python代码演示一下

以下是一个简单的Python代码演示SAC算法的微网在线优化：

import gym
import numpy as np
import torch
import torch.nn.functional as F
import torch.optim as optim
from torch import nn
from torch.distributions.normal import Normal
from torch.utils.data import DataLoader, Dataset

# 定义微网环境
class Microgrid(gym.Env):
    def __init__(self):
        self.action_space = gym.spaces.Box(low=-1, high=1, shape=(1,), dtype=np.float32)
        self.observation_space = gym.spaces.Box(low=0, high=1, shape=(1,), dtype=np.float32)
        self.state = np.random.uniform(0, 1)
        self.ref = 0.5
        self.cost = 0
        
    def step(self, action):
        # 更新状态
        self.state += action
        self.state = np.clip(self.state, 0, 1)
        # 计算奖励和成本
        reward = 1 - abs(self.state - self.ref)
        cost = abs(action) * 0.1
        self.cost += cost
        # 判断是否结束
        done = False
        if self.cost > 10:
            done = True
        # 返回状态、奖励、是否结束、调试信息
        return self.state, reward, done, {}
        
    def reset(self):
        self.state = np.random.uniform(0, 1)
        self.cost = 0
        return self.state

# 定义神经网络
class Policy(nn.Module):
    def __init__(self):
        super(Policy, self).__init__()
        self.fc1 = nn.Linear(1, 32)
        self.fc2 = nn.Linear(32, 32)
        self.mu_head = nn.Linear(32, 1)
        self.sigma_head = nn.Linear(32, 1)
        
    def forward(self, x):
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        mu = torch.tanh(self.mu_head(x))
        sigma = F.softplus(self.sigma_head(x))
        return mu, sigma

# 定义数据集
class ReplayBuffer(Dataset):
    def __init__(self, capacity):
        self.capacity = capacity
        self.buffer = []
        
    def __len__(self):
        return len(self.buffer)
        
    def __getitem__(self, index):
        return self.buffer[index]
        
    def push(self, state, action, reward, next_state, done):
        if len(self.buffer) < self.capacity:
            self.buffer.append(None)
        self.buffer[len(self.buffer)-1] = (state, action, reward, next_state, done)
        
    def sample(self, batch_size):
        return zip(*random.sample(self.buffer, batch_size))

# 定义SAC算法
class SAC:
    def __init__(
        self,
        env,
        buffer_capacity=10000,
        batch_size=128,
        gamma=0.99,
        tau=0.005,
        alpha=0.2
    ):
        self.env = env
        self.buffer = ReplayBuffer(buffer_capacity)
        self.batch_size = batch_size
        self.gamma = gamma
        self.tau = tau
        self.alpha = alpha
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        
        self.policy = Policy().to(self.device)
        self.q1 = nn.Linear(2, 1).to(self.device)
        self.q2 = nn.Linear(2, 1).to(self.device)
        self.q_target1 = nn.Linear(2, 1).to(self.device)
        self.q_target2 = nn.Linear(2, 1).to(self.device)
        
        self.q_target1.load_state_dict(self.q1.state_dict())
        self.q_target2.load_state_dict(self.q2.state_dict())
        
        self.policy_optim = optim.Adam(self.policy.parameters(), lr=1e-3)
        self.q_optim1 = optim.Adam(self.q1.parameters(), lr=1e-3)
        self.q_optim2 = optim.Adam(self.q2.parameters(), lr=1e-3)
        
    def select_action(self, state):
        state = torch.FloatTensor(state).unsqueeze(0).to(self.device)
        with torch.no_grad():
            mu, sigma = self.policy(state)
            dist = Normal(mu, sigma)
            action = dist.sample()
        return action.cpu().numpy()[0, 0]
        
    def update(self):
        if len(self.buffer) < self.batch_size:
            return
            
        state, action, reward, next_state, done = self.buffer.sample(self.batch_size)
        state = torch.FloatTensor(state).to(self.device)
        action = torch.FloatTensor(action).unsqueeze(1).to(self.device)
        reward = torch.FloatTensor(reward).unsqueeze(1).to(self.device)
        next_state = torch.FloatTensor(next_state).to(self.device)
        done = torch.FloatTensor(1 - done).unsqueeze(1).to(self.device)
        
        with torch.no_grad():
            next_mu, next_sigma = self.policy(next_state)
            next_dist = Normal(next_mu, next_sigma)
            next_action = next_dist.sample()
            q1_next_target = self.q_target1(torch.cat([next_state, next_action], 1))
            q2_next_target = self.q_target2(torch.cat([next_state, next_action], 1))
            q_next_target = torch.min(q1_next_target, q2_next_target) - self.alpha * next_dist.log_prob(next_action)
            q_target = reward + self.gamma * done * q_next_target
            
        q1 = self.q1(torch.cat([state, action], 1))
        q2 = self.q2(torch.cat([state, action], 1))
        q1_loss = F.mse_loss(q1, q_target.detach())
        q2_loss = F.mse_loss(q2, q_target.detach())
        policy_loss = (self.alpha * next_dist.log_prob(next_action) - torch.min(q1_next_target, q2_next_target)).mean()
        
        self.q_optim1.zero_grad()
        q1_loss.backward()
        self.q_optim1.step()
        
        self.q_optim2.zero_grad()
        q2_loss.backward()
        self.q_optim2.step()
        
        self.policy_optim.zero_grad()
        policy_loss.backward()
        self.policy_optim.step()
        
        for param, target_param in zip(self.q1.parameters(), self.q_target1.parameters()):
            target_param.data.copy_(self.tau * param.data + (1 - self.tau) * target_param.data)
            
        for param, target_param in zip(self.q2.parameters(), self.q_target2.parameters()):
            target_param.data.copy_(self.tau * param.data + (1 - self.tau) * target_param.data)
        
    def train(self, epochs=100):
        state = self.env.reset()
        for i in range(epochs):
            action = self.select_action(state)
            next_state, reward, done, _ = self.env.step(action)
            self.buffer.push(state, action, reward, next_state, done)
            state = next_state
            
            self.update()
            
            if done:
                state = self.env.reset()
                
            print("Epoch: {}, Reward: {}, Cost: {}".format(i, reward, self.env.cost))
            
        self.env.close()

if __name__ == "__main__":
    env = Microgrid()
    sac = SAC(env)
    sac.train()

上述代码演示了一个简单的微网在线优化问题，其中SAC算法被用于学习最优的控制策略。在训练过程中，模型通过与微网环境交互，不断优化自己的参数，以最大化微网的经济效益。