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problem with simulation trained DRL agent

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Hello,
I implemented deep reinforcement learning in Matlab based on a custom template and saved some agents with high rewards. I was plotting signals in the training phase in each episode and can see the desired performance. I saved all state and control effort (Action) in each episode. My action space is as follows:
numAct = 1;
ActionInfo = rlNumericSpec([numAct 1], 'LowerLimit' ,-0.4189, 'UpperLimit' ,0.4189);
I have a problem with the simulation of the trained agent.
The first figure is one of the results of a training phase and part of the variation of it's action value.
After the simulation, with the below command,
simOptions = rlSimulationOptions('MaxSteps',maxSteps);
experience = sim(env,agent,simOptions);
or for saved agent
experience = sim(env,saved_agent,simOptions);
The result is wrong according to the below figure.
I checked the final agent and some of the high rewards agents. But, the results are similar to the above figure.
After the simulation of the trained agent the action is fixed to lower or upper values of action space acording to above figure for all simulated agents!
Thank you for any help you can offer.

Answers (1)

Emmanouil Tzorakoleftherakis
Hello,
Please see this post that goes over a few potential reasons for discrepancies between training results and simulation results.
Looking at the actions and plots above, it seems to me that agent stopped epxloring somewhere along the way (in which case you would need to adjust exploration options in your custom algorithm). Make sure to also keep track of the individual episode rewards to get an idea of which agents lead to higher rewards.
  3 Comments
Emmanouil Tzorakoleftherakis
Edited: Emmanouil Tzorakoleftherakis on 4 Jan 2021
If you have these settings right, it may not be an exploration issue. You are saying that if the target us further away the robot does not reach it - could it be that the problem is not feasible, i.e. the target is too far away to reach within a single episode? If that's the case, maybe increasing the episode duration or adjusting action limits (if any) may help.

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