深度强化学习matlab代码

以下是一个简单的深度强化学习的 Matlab 代码示例，使用深度 Q 学习算法来解决 OpenAI Gym 环境中的 Cartpole 任务：

%% 导入 OpenAI Gym 环境
env = gym.make('CartPole-v0');

%% 建立深度 Q 学习模型
inputSize = env.observation_space.shape(2);
numActions = env.action_space.n;
hiddenSize = 10;

layers = [
    imageInputLayer([1 1 inputSize],'Normalization','none','Name','state')
    fullyConnectedLayer(hiddenSize,'Name','fc1')
    reluLayer('Name','relu1')
    fullyConnectedLayer(numActions,'Name','fc2')
    ];

lgraph = layerGraph(layers);

%% 设置深度 Q 学习算法超参数
maxEpisodes = 500;
maxSteps = 200;
epsilon = 0.1;
epsilonDecay = 0.99;
gamma = 0.99;
alpha = 0.1;

%% 训练深度 Q 学习模型
state = reset(env);
totalReward = 0;

for episode = 1:maxEpisodes
    episodeReward = 0;
    epsilon = epsilon * epsilonDecay;
    
    for step = 1:maxSteps
        % 以 epsilon-greedy 策略选择动作
        if rand < epsilon
            action = randi(numActions);
        else
            stateInput = imresize(state,[1 1 inputSize]);
            action = predict(dqn,stateInput);
            [~,action] = max(action);
        end
        
        % 执行动作并观察新状态和奖励
        [nextState,reward,done,~] = step(env,action);
        episodeReward = episodeReward + reward;
        
        % 更新 Q 值
        nextQ = predict(dqn,imresize(nextState,[1 1 inputSize]));
        targetQ = reward + gamma * max(nextQ);
        stateInput = imresize(state,[1 1 inputSize]);
        dqn = trainNetwork(stateInput,targetQ,dqn.LearnRateSchedule.LearnRates);
        
        % 更新状态
        state = nextState;
        
        % 如果游戏结束，跳出循环
        if done
            break
        end
    end
    
    % 记录总奖励
    totalReward = totalReward + episodeReward;
end

%% 测试深度 Q 学习模型
state = reset(env);
totalReward = 0;

for step = 1:maxSteps
    % 以 greedy 策略选择动作
    stateInput = imresize(state,[1 1 inputSize]);
    action = predict(dqn,stateInput);
    [~,action] = max(action);
    
    % 执行动作并观察新状态和奖励
    [nextState,reward,done,~] = step(env,action);
    totalReward = totalReward + reward;
    
    % 更新状态
    state = nextState;
    
    % 如果游戏结束，跳出循环
    if done
        break
    end
end

%% 输出总奖励
disp(['Total reward: ' num2str(totalReward)]);

请注意，这只是一个简单的示例代码，可能需要根据实际情况进行修改和优化。