mohitsingh_21 asked . 2021-05-28

Importing pre-trained recurrent network to reinforcement learning agent

% Load actor
load(netDir);
actorNetwork = net.Layers;    
actorOpts    = rlRepresentationOptions('LearnRate',learnRate);
actor        = rlStochasticActorRepresentation(actorNetwork,obsInfo,actInfo,'Observation',{'input'},actorOpts);

% Create critic
criticNetwork = [sequenceInputLayer(numObs,"Name","input")
                 lstmLayer(numObs)
                 softplusLayer()
                 fullyConnectedLayer(1)];
criticOpts = rlRepresentationOptions('LearnRate',learnRate);
critic     = rlValueRepresentation(criticNetwork,obsInfo,'Observation',{'input'},criticOpts);

% Create agent
agentOpts = rlPPOAgentOptions('ExperienceHorizon',expHorizon, 'MiniBatchSize',miniBatchSz, 'NumEpoch',nEpoch, 'ClipFactor', 0.1);
agent     = rlPPOAgent(actor,critic,agentOpts);

% Train agent
trainOpts = rlTrainingOptions('MaxEpisodes',episodes, 'MaxStepsPerEpisode',episodeSteps,    ...
                              'Verbose',false, 'Plots','training-progress',                 ...
                              'StopTrainingCriteria', 'AverageReward',                      ...
                              'StopTrainingValue',10);

% Run training
trainingStats = train(agent,env,trainOpts);

% Simulate
simOptions  = rlSimulationOptions('MaxSteps',2000);
experience  = sim(env,agent,simOptions);

deep learning , simulink , matlab