When training an agent using the Reinforcement Learning Toolbox, how can I use a custom stopping criterion?
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The current options only allow for 5 predefined choices ("AverageSteps", "AverageReward", "EpisodeReward", "GlobalStepCount", "EpisodeCount"). I want to include a stopping criterion different from these. Is there any option to do the same?
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goc3
on 14 Jul 2020
I was about to ask a similar question... The "accepted" answer below doesn't actually answer the question—instead, it confirms that those are the only available stop criteria.
It would be great if additional options and/or support for custom stopping criteria were added.
As an example, for a particular application, I would like to stop training once the episode reward plateaus. It is not known beforehand at what value it will plateau, so having to set a constant before training is very limiting for any application that is programmed to be dynamic or to proceed automatically based on training results.
Answers (1)
Rajani Mishra
on 6 Jan 2020
trainOpts = rlTrainingOptions(Name,Value) creates an option set for training using specified name-value pairs.
Arguments like - 'StopTrainingCriteria', 'StopTrainingValue', 'MaxEpisodes' should be specified for defining stopping criterion while training an agent.
- StopTrainingCriteria: Specifies the termination condition. Takes one of the choices as you have mentioned
- StopTrainingValue: Specifies the Critical value of training termination condition. Training terminates when the termination condition specified by the StopTrainingCriteria option equals or exceeds this value
- MaxEpisodes: Specifies maximum number of episodes to train the agent, once the number of episodes reached training terminates
For more information please refer to
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Tuwe Löfström
on 13 Jul 2020
So there is no way of adding a custom stopping criteria, in a similar way as you can define custom reset and step functions?
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