基于pytorch的sac连续空间的算法，并输出每个网络训练模型pth文件的代码。要求给出例程分段展示这个算法，并对该算法进行解释

以下是基于PyTorch的SAC算法的代码，包括了训练和保存模型的部分：

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

# 神经网络定义
class QNetwork(nn.Module):
    def __init__(self, state_dim, action_dim, hidden_dim):
        super(QNetwork, self).__init__()
        self.fc1 = nn.Linear(state_dim + action_dim, hidden_dim)
        self.fc2 = nn.Linear(hidden_dim, hidden_dim)
        self.fc3 = nn.Linear(hidden_dim, 1)

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


class SACAgent:
    def __init__(self, state_dim, action_dim, hidden_dim, gamma=0.99, tau=0.005,
                 alpha=0.2, alpha_lr=3e-4, q_lr=3e-4, policy_lr=3e-4):
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

        self.state_dim = state_dim
        self.action_dim = action_dim
        self.hidden_dim = hidden_dim

        self.gamma = gamma  # 折扣因子
        self.tau = tau  # 目标网络的更新速率
        self.alpha = alpha  # 温度参数
        self.alpha_lr = alpha_lr  # 温度参数的学习率
        self.q_lr = q_lr  # Q网络的学习率
        self.policy_lr = policy_lr  # 策略网络的学习率

        # 创建Q网络和目标Q网络
        self.q_net1 = QNetwork(state_dim, action_dim, hidden_dim).to(self.device)
        self.q_net2 = QNetwork(state_dim, action_dim, hidden_dim).to(self.device)
        self.target_q_net1 = QNetwork(state_dim, action_dim, hidden_dim).to(self.device)
        self.target_q_net2 = QNetwork(state_dim, action_dim, hidden_dim).to(self.device)
        self.target_q_net1.load_state_dict(self.q_net1.state_dict())
        self.target_q_net2.load_state_dict(self.q_net2.state_dict())

        # 创建策略网络和目标策略网络
        self.policy_net = nn.Sequential(
            nn.Linear(state_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, action_dim)
        ).to(self.device)
        self.target_policy_net = nn.Sequential(
            nn.Linear(state_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, action_dim)
        ).to(self.device)
        self.target_policy_net.load_state_dict(self.policy_net.state_dict())

        # 创建温度参数
        self.log_alpha = torch.tensor(np.log(alpha)).to(self.device)
        self.log_alpha.requires_grad = True
        self.alpha_optimizer = optim.Adam([self.log_alpha], lr=alpha_lr)

        # 创建优化器
        self.q_optimizer1 = optim.Adam(self.q_net1.parameters(), lr=q_lr)
        self.q_optimizer2 = optim.Adam(self.q_net2.parameters(), lr=q_lr)
        self.policy_optimizer = optim.Adam(self.policy_net.parameters(), lr=policy_lr)

    def get_action(self, state):
        state = torch.FloatTensor(state).unsqueeze(0).to(self.device)
        action_mean = self.policy_net(state)
        cov_mat = torch.diag_embed(torch.ones(self.action_dim)).unsqueeze(0).to(self.device)
        dist = torch.distributions.multivariate_normal.MultivariateNormal(action_mean, cov_mat)
        action = dist.sample()
        return action.detach().cpu().numpy()[0]

    def update(self, replay_buffer, batch_size):
        state, action, next_state, reward, done = replay_buffer.sample(batch_size)
        state = torch.FloatTensor(state).to(self.device)
        action = torch.FloatTensor(action).to(self.device)
        next_state = torch.FloatTensor(next_state).to(self.device)
        reward = torch.FloatTensor(reward).to(self.device).unsqueeze(1)
        done = torch.FloatTensor(done).to(self.device).unsqueeze(1)

        # 更新Q网络
        q1 = self.q_net1(state, action)
        q2 = self.q_net2(state, action)
        with torch.no_grad():
            next_action_mean = self.target_policy_net(next_state)
            cov_mat = torch.diag_embed(torch.ones(self.action_dim)).unsqueeze(0).to(self.device)
            next_dist = torch.distributions.multivariate_normal.MultivariateNormal(next_action_mean, cov_mat)
            next_action = next_dist.sample()
            next_q1 = self.target_q_net1(next_state, next_action)
            next_q2 = self.target_q_net2(next_state, next_action)
            next_q = torch.min(next_q1, next_q2)
            q_target = reward + (1 - done) * self.gamma * (next_q - self.alpha * next_dist.log_prob(next_action).unsqueeze(1))
        q1_loss = nn.functional.mse_loss(q1, q_target.detach())
        q2_loss = nn.functional.mse_loss(q2, q_target.detach())
        self.q_optimizer1.zero_grad()
        q1_loss.backward()
        self.q_optimizer1.step()
        self.q_optimizer2.zero_grad()
        q2_loss.backward()
        self.q_optimizer2.step()

        # 更新策略网络
        new_action_mean = self.policy_net(state)
        new_cov_mat = torch.diag_embed(torch.ones(self.action_dim)).unsqueeze(0).to(self.device)
        new_dist = torch.distributions.multivariate_normal.MultivariateNormal(new_action_mean, new_cov_mat)
        new_action = new_dist.sample()
        new_q1 = self.q_net1(state, new_action)
        new_q2 = self.q_net2(state, new_action)
        policy_loss = (self.alpha * new_dist.log_prob(new_action).unsqueeze(1) - torch.min(new_q1, new_q2)).mean()
        self.policy_optimizer.zero_grad()
        policy_loss.backward()
        self.policy_optimizer.step()

        # 更新温度参数
        alpha_loss = -(self.log_alpha * (new_dist.log_prob(new_action).unsqueeze(1) + self.target_entropy).detach()).mean()
        self.alpha_optimizer.zero_grad()
        alpha_loss.backward()
        self.alpha_optimizer.step()
        self.alpha = self.log_alpha.exp()

        # 更新目标网络
        for target_param, param in zip(self.target_q_net1.parameters(), self.q_net1.parameters()):
            target_param.data.copy_(self.tau * param.data + (1 - self.tau) * target_param.data)
        for target_param, param in zip(self.target_q_net2.parameters(), self.q_net2.parameters()):
            target_param.data.copy_(self.tau * param.data + (1 - self.tau) * target_param.data)
        for target_param, param in zip(self.target_policy_net.parameters(), self.policy_net.parameters()):
            target_param.data.copy_(self.tau * param.data + (1 - self.tau) * target_param.data)

    def save_model(self, path):
        torch.save({
            'q_net1_state_dict': self.q_net1.state_dict(),
            'q_net2_state_dict': self.q_net2.state_dict(),
            'target_q_net1_state_dict': self.target_q_net1.state_dict(),
            'target_q_net2_state_dict': self.target_q_net2.state_dict(),
            'policy_net_state_dict': self.policy_net.state_dict(),
            'target_policy_net_state_dict': self.target_policy_net.state_dict(),
            'log_alpha': self.log_alpha
        }, path)

SAC算法是一种 actor-critic 方法，它同时学习策略和 Q 值函数。其中，Q 值函数有两个，分别用于评估当前策略和作为目标策略的参考。SAC算法也使用了温度参数来控制策略熵，从而平衡探索和利用。在更新策略网络时，SAC算法使用了重参数化技巧，将策略网络的输出重新参数化为一个均值和一个协方差矩阵，从而使得策略梯度可以被有效地计算。此外，SAC算法还使用了目标网络和延迟更新的方法来提高学习的稳定性。

以上代码实现了SAC算法的核心部分，包括了Q网络、策略网络、温度参数和目标网络的定义，以及重要的更新函数。同时，代码还提供了保存模型的方法，以便将训练好的模型保存到磁盘上。