提供一个DQN的MATLAB代码

以下是一个简单的DQN（Deep Q-Network）的MATLAB实现。这个代码使用了经典的CartPole环境作为示例。在这个环境中，一个杆子被放在一个小车上，目标是通过左右移动小车来保持杆子直立。我们用一个神经网络来估计每个动作的Q值，并使用经验回放和目标网络来训练网络。

注意：这个代码仅供参考，可能需要根据实际情况进行修改和优化。

% Load the CartPole environment
env = rlPredefinedEnv("CartPole-Discrete");

% Define the Q-network
stateSize = numel(env.reset);
numActions = numel(env.actioninfo);
numHiddenUnits = 16;
layers = [ ...
    imageInputLayer([1 1 stateSize],'Normalization','none','Name','observation')
    fullyConnectedLayer(numHiddenUnits,'Name','fc1')
    reluLayer('Name','relu1')
    fullyConnectedLayer(numHiddenUnits,'Name','fc2')
    reluLayer('Name','relu2')
    fullyConnectedLayer(numActions,'Name','output')];
lgraph = layerGraph(layers);

% Define the DQN agent
agentOptions = rlDQNAgentOptions( ...
    'SampleTime',env.Ts, ...
    'TargetUpdateFrequency',100, ...
    'ExperienceBufferLength',100000, ...
    'DiscountFactor',0.99, ...
    'Epsilon',1, ...
    'EpsilonDecay',1e-6, ...
    'MiniBatchSize',64);
dqn = rlDQNAgent(lgraph,agentOptions);

% Train the agent
maxEpisodes = 1000;
maxSteps = ceil(env.Tf/env.Ts);
trainOpts = rlTrainingOptions( ...
    'MaxEpisodes',maxEpisodes, ...
    'MaxStepsPerEpisode',maxSteps, ...
    'Verbose',false, ...
    'Plots','training-progress');
trainingStats = train(dqn,env,trainOpts);