feat: add vloss clipping to jax ppo.

[KaleabTessera]

Description

Added value loss clipping to Jax ppo. I noticed it was there in the pytorch implementation and not in the Jax implementation, and it made quite a big performance difference on the environments I tested.

Types of changes

• [ ] Bug fix
• [x] New feature
• [ ] New algorithm
• [ ] Documentation

Checklist:

• [x] I've read the CONTRIBUTION guide (required).
• [x] I have ensured pre-commit run --all-files passes (required).
• [ ] I have updated the tests accordingly (if applicable).
• [ ] I have updated the documentation and previewed the changes via mkdocs serve.
  • [ ] I have explained note-worthy implementation details.
  • [ ] I have explained the logged metrics.
  • [ ] I have added links to the original paper and related papers.

If you need to run benchmark experiments for a performance-impacting changes:

• [ ] I have contacted @vwxyzjn to obtain access to the openrlbenchmark W&B team.
• [ ] I have used the benchmark utility to submit the tracked experiments to the openrlbenchmark/cleanrl W&B project, optionally with --capture-video.
• [ ] I have performed RLops with python -m openrlbenchmark.rlops.
  • For new feature or bug fix:
    • [ ] I have used the RLops utility to understand the performance impact of the changes and confirmed there is no regression.
  • For new algorithm:
    • [ ] I have created a table comparing my results against those from reputable sources (i.e., the original paper or other reference implementation).
  • [ ] I have added the learning curves generated by the python -m openrlbenchmark.rlops utility to the documentation.
  • [ ] I have added links to the tracked experiments in W&B, generated by python -m openrlbenchmark.rlops ....your_args... --report, to the documentation.

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Name	Status	Preview	Comments	Updated (UTC)
cleanrl	✅ Ready (Inspect)	Visit Preview	💬 Add feedback	Oct 27, 2023 11:52am