How to save an rl agent after every 1000 episodes?

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I am training a DDPG agent where the training runs over 1000 episodes. To see how it evolves, I would like to save the agents after every 1000 episodes. As i see the options available in rlTrainingOptions, it is only possible to save every agent after a critical value. This slows down the training process significantly because saving every agent consumes a lot of time. Is there an efficient way to save the agents only after every 1000 episodes?
Thank you.
  1 Comment
Heesu Kim
Heesu Kim on 12 Mar 2021
Edited: Heesu Kim on 19 Mar 2021
I agree with this. I don't understand why it does not have the most useful option. And I'm disappointed that this question still doesn't have any answer.

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Accepted Answer

Madhav Thakker
Madhav Thakker on 19 Mar 2021
Hi Guru,
I understand currently in rlTrainingOptions, there is no option to save the agent after specific number of episodes. I have raised an enhancement request for the same and this might be considered in future releases.
Hope this helps.

More Answers (2)

Manuel Sebastian Rios Beltran
@Madhav Thakker But they did not do it :( a year later

Lance on 23 Jun 2023
Edited: Lance on 29 Jun 2023
From what I understand, the only other work around would be to write another training command. You would have to predfine this for every "checkpoint" ie. 10,20,30 episodes. The training-progress graph will continue to be actively updated. (Note I am using R2022a)
% Define all agents, observations, actions, environment, etc....
trainingStats=train([agent1,agent2],environment,trainingOpts); % Will train to max episodes and save agent
% Edit Trainingoptions to increase maxepisodes and save agent value
% Resume training -- Will train to 1000 episodes and save agent again
trainingStats2=train([agent1,agent2],environment,trainingStats) %Note you use trainingStats here not trainingOpts
Let me know if this helps!




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