How to save an rl agent after every 1000 episodes?

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.

[Heesu Kim]

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.

Answer 1

[Madhav Thakker]

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.

[Heesu Kim]

Yay!

[Dmitriy Ogureckiy]

I am the same opinion. Add this, please. Ball on your side.

Answer 2

[Manuel Sebastian Rios Beltran]

@Madhav Thakker But they did not do it :( a year later

[Matteo D'Ambrosio]

2 years later! Agreed that this enhancement is much needed...

Answer 3

[Lance]

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....
maxepisodes=500;
trainingOpts=rlMultiAgentTrainingOptions;
trainingOpts.SaveAgentCriteria="EpisodeCount";
trainingOpts.SaveAgentValue=maxepisodes
trainingStats=train([agent1,agent2],environment,trainingOpts); % Will train to max episodes and save agent

% Edit Trainingoptions to increase maxepisodes and save agent value

trainingStats(1,1).TrainingOptions.MaxEpisodes=1000;
trainingStats(1,1).TrainingOptions.SaveAgentValue=[1000,1000];
trainnigStats(1,1).TrainingOptions.StopTrainingValue=[1000,1000];
trainingStats(1,2).TrainingOptions.MaxEpisodes=1000;
trainingStats(1,2).TrainingOptions.SaveAgentValue=[1000,1000];
trainnigStats(1,2).TrainingOptions.StopTrainingValue=[1000,1000];

% 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!