In this PR we have added the possibility to better control all logging and checkpointing (for performance reasons). In particular:
The user can now control what and how to log directly from the configs. Moreover, every algorithm specifies what it will be logged by default in the set AGGREGATOR_KEYS defined under the sheeprl/algos/{algorithm}/utils.py: only the metrics present in both will be logged at runtime
The user can now specify whether to save or not the last checkpoint at the end of the training
A new howto on how to control logging and checkpoint has been added
Type of Change
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New feature (non-breaking change that adds functionality)
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Summary
In this PR we have added the possibility to better control all logging and checkpointing (for performance reasons). In particular:
AGGREGATOR_KEYSdefined under thesheeprl/algos/{algorithm}/utils.py: only the metrics present in both will be logged at runtimeType of Change
Please select the one relevant option below:
Checklist
Please confirm that the following tasks have been completed:
Thank you for your contribution! Once you have filled out this template, please ensure that you have assigned the appropriate reviewers and that all tests have passed.