Reinforcement Learning with Parallel Computing

Hi All,

I have been training a TD3 RNN agent on my local PC for montrhs now, due to the long training period due to the performance of my PC I have been saving the buffer, so can I can reload the pretrained agent to restart training.

I now have access to my University HPC server, so can now use parallel computing to speed up the training process.

However, now when I attempt run the restart training with the pretrained agent, now with parallel computing on the HPC server, (which has prevously been running on my local PC with no issues with NO parallel computing) it flags the following issue.

Do I need to start with a fresh agent now I am using parallel computing?

Also is the following code to start parallel computing correct?

% trainingOpts.UseParallel = true;
% trainingOpts.ParallelizationOptions.Mode = 'async';
% trainingOpts.ParallelizationOptions.DataToSendFromWorkers = 'Experiences';

Thanks

Patrick

Answer 1

[Drew Davis]

As of R2021a, the RL Toolbox does not support parallel training with RNN networks.

You can still reuse your current experience buffer for training new networks by replacing the actor and critic for TD3

agent.AgentOptions.ResetExperienceBufferBeforeTraining = false;
setActor(agent,statelessActor);
setCritic(agent,statelessCritic);

Your snippet to setup TD3 parallel training looks good.

Hope this helps

Drew

[PB75]

Hi Drew,

Thanks for your reply, so I cannot use LSTM layers with parallel training?