When an experiment is conducted using the evaluation callback, the evaluations during intermediate learning processes do not use the learned calibration of normalized variables from the training and instead update on their own.
For an evaluation, the model should use the same calibration (without updating) and start from the values at the point where the training is interrupted. This patch fixes that functionality to ensure this happens.
Due to the recent update from the opyplus package, as mentioned in issue #418, this update caused an error by breaking the dependency system on internal packages. The calls to these packages with new names in Sinergym have been fixed.
Since this update made opyplus compatible with new Python versions, we have taken advantage of this update to become compatible with the new versions of Ubuntu and Python (24.04 and 3.12.3), making us officially compatible.
Fixes #418
Types of changes
[x] Bug fix (non-breaking change which fixes an issue)
[ ] New feature (non-breaking change which adds functionality)
[ ] Breaking change (fix or feature that would cause existing functionality to change)
[ ] My change requires a change to the documentation.
[ ] I have updated the tests.
[ ] I have updated the documentation accordingly.
[ ] I have reformatted the code using autopep8 second level aggressive.
[ ] I have reformatted the code using isort.
[ ] I have ensured cd docs && make spelling && make html pass (required if documentation has been updated.)
[ ] I have ensured pytest tests/ -vv pass. (required).
[ ] I have ensured pytype -d import-error sinergym/ pass. (required)
Changelog:
Fix EvalLoggerCallback for deactivate normalization update and update mean and var calibration from training env automatically (when NormalizeObservation was activated)
Fix opyplus update inconsistency.
Dockerfile: Updated Ubuntu and Python version (24.04 and 3.12.3)
Description
When an experiment is conducted using the evaluation callback, the evaluations during intermediate learning processes do not use the learned calibration of normalized variables from the training and instead update on their own.
For an evaluation, the model should use the same calibration (without updating) and start from the values at the point where the training is interrupted. This patch fixes that functionality to ensure this happens.
Due to the recent update from the opyplus package, as mentioned in issue #418, this update caused an error by breaking the dependency system on internal packages. The calls to these packages with new names in Sinergym have been fixed.
Since this update made opyplus compatible with new Python versions, we have taken advantage of this update to become compatible with the new versions of Ubuntu and Python (24.04 and 3.12.3), making us officially compatible.
Fixes #418
Types of changes
Checklist:
autopep8second level aggressive.isort.cd docs && make spelling && make htmlpass (required if documentation has been updated.)pytest tests/ -vvpass. (required).pytype -d import-error sinergym/pass. (required)Changelog:
EvalLoggerCallbackfor deactivate normalization update and update mean and var calibration from training env automatically (when NormalizeObservation was activated)