dqn算法基础代码matlab

你好！以下是一个使用DQN算法实现的基础代码示例（MATLAB）：

% 导入依赖库
import rl.agents.dqn.*
import rl.util.*

% 创建环境
env = rlPredefinedEnv("BasicGridWorld");

% 创建DQN网络
inputSize = prod(env.ObservationInfo.Dimension);
numActions = numel(env.ActionInfo);
layers = [
    imageInputLayer([inputSize 1 1],'Normalization','none','Name','state')
    fullyConnectedLayer(100,'Name','CriticStateFC1')
    reluLayer('Name','CriticRelu1')
    fullyConnectedLayer(50,'Name','CriticStateFC2')];
criticNetwork = [
    layers
    fullyConnectedLayer(numActions,'Name','output')];
criticOpts = rlRepresentationOptions('LearnRate',1e-03,'GradientThreshold',1);

% 创建DQN代理
agentOpts = rlDQNAgentOptions(...
    'SampleTime',1,...
    'TargetUpdateMethod',"periodic",...
    'TargetUpdateFrequency',4,...
    'ExperienceBufferLength',100000,...
    'DiscountFactor',0.99,...
    'MiniBatchSize',48);
agentOpts.EpsilonGreedyExploration.EpsilonDecay = 1e-4;
agent = rlDQNAgent(criticNetwork,env,agentOpts);

% 参数配置
maxEpisodes = 1000;
maxSteps = ceil(env.MaxStep/maxEpisodes);

% 训练代理
trainingStats = train(agent,env,maxEpisodes,maxSteps);

% 测试代理
envTest = rlPredefinedEnv("BasicGridWorld");
simOptions = rlSimulationOptions('MaxSteps',maxSteps);
experiences = sim(envTest,agent,simOptions);

% 绘制训练曲线
plot(trainingStats.EpisodeReward)
ylabel('Total Reward')
xlabel('Episode')

希望这个基础代码能帮到你！如果还有其他问题，请随时提问。