why I get a different action result every new time with same sample observations after deploying trained RL policies?
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de y
on 23 Feb 2021
Edited: liang zhang
on 2 Mar 2022
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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Accepted Answer
Emmanouil Tzorakoleftherakis
on 23 Feb 2021
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.
1 Comment
liang zhang
on 2 Mar 2022
Edited: liang zhang
on 2 Mar 2022
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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