ddpg算法的matlab代码详细讲解

### DDPG算法的MATLAB代码实现与详解 #### 1. 初始化参数和环境设置 为了使DDPG能够在MATLAB中运行，首先需要设定一些基本参数以及初始化环境。 ```matlab % 参数配置 MAX_EPISODES = 200; MAX_EP_STEPS = 200; MEMORY_CAPACITY = 1e4; var = 3; % 初始方差用于增强actor探索性 env = gym.make('Maze-v0'); % 创建迷宫环境实例[^1] state_dim = size(env.observation_space.high); action_dim = size(env.action_space.high); % 构建记忆库 memory = Memory(MEMORY_CAPACITY, state_dim, action_dim); ``` #### 2. 定义Actor-Critic网络结构 接着定义两个主要组成部分——Actor（策略网络）和Critic（价值评估网络）。这里采用两层全连接神经网络作为基础架构。 ```matlab function net = createNetwork(inputSize,outputSize) layers = [ featureInputLayer(inputSize,'Name','input') fullyConnectedLayer(30,'WeightLearnRateFactor',0.001,... 'BiasLearnRateFactor',0.002,'Name','fc1') reluLayer('Name','relu1') fullyConnectedLayer(outputSize,'WeightLearnRateFactor',... 0.001,'BiasLearnRateFactor',0.002,'Name','fc2')]; options = trainingOptions('adam',... 'MaxEpochs',50,... 'MiniBatchSize',64,... 'Plots','training-progress'); net = dlnetwork(layers); end actor_net = createNetwork(state_dim, action_dim); critic_net = createNetwork([state_dim + action_dim], 1); ``` #### 3. 学习过程描述 在每次迭代过程中，先执行动作并观察新状态及奖励值，随后存储这些经验到记忆库中。当记忆库积累足够多的经验后，则从中随机抽取一批样本进行训练。 ```matlab for episode = 1:MAX_EPISODES s = env.reset(); ep_reward = 0; for t = 1:MAX_EP_STEPS a = predict(actor_net,dlnarray(s)); a_with_noise = clip(randn(size(a))*var+a,-2,2); % 添加噪声增加探索能力 [next_state,reward,is_done,_] = env.step(double(a_with_noise)); memory.addExperience({s,a_with_noise,reward/10,next_state}); if length(memory.experiences)>=MEMORY_CAPACITY && mod(t,5)==0 batch_data = memory.sampleBatch(BATCH_SIZE); states_batch = cat(3,batch_data{:,1}); actions_batch = cat(3,batch_data{:,2}); rewards_batch = cat(3,batch_data{:,3}); next_states_batch = cat(3,batch_data{:,4}); target_q_values = double(predict(critic_net,[dlnarray(next_states_batch),... predict(target_actor_net,dlnarray(next_states_batch))])); y_i = squeeze(rewards_batch)+gamma*target_q_values; critic_loss = mse(dlnarray(y_i)-predict(critic_net,... [dlnarray(states_batch),dlnarray(actions_batch)])); adamupdate(critic_net.Learnables,critic_optim.State,... critic_loss,"iteration",t); [~,grad]=dlgradient(critic_loss,critic_net.Learnables.Weights); policy_grad=-mean(grad,3)'; adamupdate(actor_net.Learnables,actor_optim.State,policy_grad,... "iteration",t); softUpdateWeights!(actor_net,target_actor_net,tau); softUpdateWeights!(critic_net,target_critic_net,tau); var=var*(1-1e-4); % 减少行动噪音幅度 end s = next_state; ep_reward = ep_reward + reward; if is_done || t==MAX_EP_STEPS disp(['Episode:',num2str(episode),' Reward:',num2str(ep_reward)]); break; end end end ``` 上述代码实现了DDPG的核心逻辑，并针对特定的任务进行了调整以适应实际应用场景的需求[^2]。