why I get a different action result every new time with same sample observations after deploying trained RL policies?
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load("agent0218_300016_40000.mat","agent");
obsInfo = getObservationInfo(agent);
actInfo = getActionInfo(agent);
ResetHandle = @() myResetFunction(test_sss);
StepHandle = @(Action,LoggedSignals) myStepFunction(Action,LoggedSignals,test_sss);
envT = rlFunctionEnv(obsInfo,actInfo,StepHandle,ResetHandle);
simOpts = rlSimulationOptions('MaxSteps',size(test_sss,1));
experience = sim(envT,agent,simOpts);
ac3=squeeze(experience.Action.bs.Data);
%******************************************************************************
%******************************************************************************
generatePolicyFunction(agent);
%******************************************************************************
%******************************************************************************
for iii=1:size(ac3,1)
observation1=test_sss{iii,:};
action1(iii,1) = evaluatePolicy(observation1);
end
sum(abs(ac3-action1))
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Emmanouil Tzorakoleftherakis
2021년 2월 23일
Which agent are you using? Some agents are stochastic, meaning that the output is sampled based on probability distributions so by construction they won't give you the same result.
Another possible reason is the reset function. It seems you are saving simulation data and running inference again, but every time you call 'sim', the reset function is called first. So if there are any components that randomize initial conditions/parameters, then you are not comparing with the same data.
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liang zhang
2022년 3월 2일
편집: liang zhang
2022년 3월 2일
I also encountered the same problem when I used the DDPG agent for verification, my reset function doesn't randomize initial any conditions/parameters,I guess if the trained DDPG agent also has its own noise? Shouldn't a trained agent be a fixed set of neural network parameters?
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