How to resume train a trained agent?about Q learning agents.

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Chao Wang
Chao Wang 2021-4-18
评论： Chao Wang 2021-4-23
When I use Q Learning training, how can I continue to use the agent's experience resume training of the agent after I stop training midway?Will I still be able to see the progress of the training as I did the first time, and modify the parameters before resuming the training again?
by the way ,After the training, how to see the value in the QTable?Every time I open the QTable, all the values are 0.
I don´t understand why... Can somebody help me out on this?
Here is my code
clc;
clear;
mdl = 'FOUR_DG_0331';
open_system(mdl);
agentBlk = ["FOUR_DG_0331/RL Agent1", "FOUR_DG_0331/RL Agent2", "FOUR_DG_0331/RL Agent3", "FOUR_DG_0331/RL Agent4"];
oInfo = rlFiniteSetSpec([123,456,789]);
aInfo = rlFiniteSetSpec([150,160,170]);
aInfo1 = rlFiniteSetSpec([150,170]);
obsInfos = {oInfo,oInfo,oInfo,oInfo};
actInfos = {aInfo1,aInfo,aInfo,aInfo};
env = rlSimulinkEnv(mdl,agentBlk,obsInfos,actInfos);
Ts = 0.01;
Tf = 4;
rng(0);
qTable1 = rlTable(oInfo,aInfo1);
qTable2 = rlTable(oInfo,aInfo);
qTable3 = rlTable(oInfo,aInfo);
qTable4 = rlTable(oInfo,aInfo);
criticOpts = rlRepresentationOptions('LearnRate',0.1);
Critic1 = rlQValueRepresentation(qTable1,oInfo,aInfo1,criticOpts);
Critic2 = rlQValueRepresentation(qTable2,oInfo,aInfo,criticOpts);
Critic3 = rlQValueRepresentation(qTable3,oInfo,aInfo,criticOpts);
Critic4 = rlQValueRepresentation(qTable4,oInfo,aInfo,criticOpts);
%/*Code here for agent option**/
%... ....
%........
agent1 = rlQAgent(Critic1,QAgent_opt);
agent2 = rlQAgent(Critic2,QAgent_opt);
agent3 = rlQAgent(Critic3,QAgent_opt);
agent4 = rlQAgent(Critic4,QAgent_opt);
trainOpts = rlTrainingOptions;
trainOpts.MaxEpisodes = 1000;
trainOpts.MaxStepsPerEpisode = ceil(Tf/Ts);
trainOpts.StopTrainingCriteria = "EpisodeCount";
trainOpts.StopTrainingValue = 1000;
trainOpts.SaveAgentCriteria = "EpisodeCount";
trainOpts.SaveAgentValue = 15;
trainOpts.SaveAgentDirectory = "savedAgents";
trainOpts.Verbose = false;
trainOpts.Plots = "training-progress";
doTraining = false;
if doTraining
stats = train([agent1, agent2, agent3, agent4],env,trainOpts);
else
load(trainOpts.SaveAgentDirectory +"/Agents16.mat",'agent');
simOpts = rlSimulationOptions('MaxSteps',ceil(Tf/Ts));
experience = sim(env,[agent1 agent2 agent3 agent4 ],simOpts)
end

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采纳的回答

Emmanouil Tzorakoleftherakis
Hello,
To see how to iew the table values, take a look at the answer here.
Also, you don't have to do anything specific to continue training where you left off - you can simply begin training again and the policy will start from whatever values/weights it had previously.

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