发一段DDPG的matlab代码

DDPG算法是一种基于Actor-Critic架构的深度强化学习算法，下面是一个简单的DDPG算法的Matlab代码实现：

% DDPG算法的matlab实现
% 算法流程：Actor-Critic架构+经验回放+延迟更新+目标网络
% 环境：Matlab R2020a

% 清空环境变量
clear all
close all
clc

% 参数设置
N = 1000; % 迭代次数
T = 100; % 每轮步数
gamma = 0.99; % 折扣因子
tau = 0.01; % 目标网络更新速率
buffer_size = 10000; % 经验回放缓存大小
batch_size = 32; % 每次训练的样本数
lr_actor = 1e-3; % Actor网络学习率
lr_critic = 1e-3; % Critic网络学习率
noise_std = 0.1; % 噪声标准差

% 环境设置
env = rlPredefinedEnv("Pendulum-Continuous");

% 状态和动作空间
state_dim = numel(env.ObservationInfo);
action_dim = numel(env.ActionInfo);

% Actor网络结构
actor_layers = [
    imageInputLayer([1 1 state_dim],'Normalization','none','Name','state')
    fullyConnectedLayer(128,'Name','FC1')
    reluLayer('Name','Relu1')
    fullyConnectedLayer(64,'Name','FC2')
    reluLayer('Name','Relu2')
    fullyConnectedLayer(action_dim,'Name','output')
    tanhLayer('Name','tanh')];
actor_options = rlRepresentationOptions('LearnRate',lr_actor,'GradientThreshold',1);

% Critic网络结构
critic_layers = [
    imageInputLayer([1 1 state_dim],'Normalization','none','Name','state')
    fullyConnectedLayer(128,'Name','FC1')
    reluLayer('Name','Relu1')
    fullyConnectedLayer(64,'Name','FC2')
    reluLayer('Name','Relu2')
    fullyConnectedLayer(action_dim,'Name','FC3')
    additionLayer(2,'Name','add')
    reluLayer('Name','Relu3')
    fullyConnectedLayer(1,'Name','output')];
critic_options = rlRepresentationOptions('LearnRate',lr_critic,'GradientThreshold',1);

% 创建Actor网络和Critic网络
actor = rlStochasticActorRepresentation(actor_layers,state_dim,action_dim,'Observation',{'state'},'Action',{'tanh'},actor_options);
critic = rlValueRepresentation(critic_layers,state_dim,action_dim,'Observation',{'state'},'Action',{'tanh'},critic_options);

% 创建DDPG智能体
agent = rlDDPGAgent(actor,critic);
agent.Options.DiscountFactor = gamma;
agent.Options.TargetSmoothFactor = tau;

% 创建经验回放缓存
buffer = rlReplayBuffer(buffer_size);

% 训练
for i = 1:N
    % 重置环境
    obs = env.reset();
    
    % 初始化ep_reward
    ep_reward = 0;
    
    % 开始训练
    for t = 1:T
        % 获取动作
        action = agent.getAction(obs);
        
        % 添加噪声
        noise = noise_std*randn(size(action));
        action = action + noise;
        
        % 执行动作
        [next_obs,reward,done] = env.step(action);
        
        % 计算当前ep_reward
        ep_reward = ep_reward + reward;
        
        % 存储经验
        buffer.add(obs,action,reward,next_obs,done);
        
        % 更新网络
        if buffer.NumExperience >= batch_size
            minibatch = buffer.getMiniBatch(batch_size);
            agent = train(agent,minibatch);
        end
        
        % 更新状态
        obs = next_obs;
        
        % 判断是否结束
        if done
            break
        end
    end
    
    % 输出结果
    fprintf("Episode %d, Reward %d\n",i,ep_reward);
end

注意，上述代码中的状态空间和动作空间都是连续的，因此使用了tanh作为Actor网络的输出激活函数。如果状态空间和动作空间是离散的，需要修改网络结构和输出激活函数。