用MATLAB写dqn代码

抱歉，作为AI语言模型，我无法提供完整的MATLAB编程实现。以下是一个简单的DQN代码示例，希望能给您提供一些帮助：

% DQN implementation in MATLAB

% Set up the environment
env = rlPredefinedEnv('CartPole-Discrete');
num_actions = getActionInfo(env).NumberOfActions;
obs_info = getObservationInfo(env);
act_info = getActionInfo(env);

% Set up the DQN agent
dqn_layers = [
imageInputLayer([1 4 1],'Normalization','none','Name','state')
fullyConnectedLayer(24,'Name','CriticFC1')
reluLayer('Name','CriticReLU1')
fullyConnectedLayer(24,'Name','CriticFC2')
reluLayer('Name','CriticReLU2')
fullyConnectedLayer(num_actions,'Name','output')];
dqn_options = rlDQNAgentOptions('SampleTime',0.1,'TargetUpdateMethod',"periodic",'TargetUpdateFrequency',4,'ExperienceBufferLength',100000,'DiscountFactor',0.99,'Epsilon',1,'EpsilonMin',0.1,'EpsilonDecay',1e-3,'LearnRate',1e-3,'GradientThreshold',1,'MiniBatchSize',32,'ExperienceBufferLength',100000);
dqn_agent = rlDQNAgent(dqn_layers,act_info,dqn_options);

% Train the agent
max_episodes = 1000;
max_steps = 500;
train_stats = train(dqn_agent,env,max_episodes,max_steps);

% Test the agent
max_steps = 500;
sim_options = rlSimulationOptions('MaxSteps',max_steps,'StopOnError','on');
experiences = sim(env,dqn_agent,sim_options);
total_reward = sum(experiences.Reward);