## Description ## Types of changes - [ ] Bug fix - [ ] New feature - [ ] New algorithm - [ ] Documentation ## Checklist: - [ ] I've read the [CONTRIBUTION](https://docs.cleanrl.dev/contribution/) guide (**required**). - [ ] 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](https://wandb.ai/openrlbenchmark). - [ ] I have used the [benchmark utility](/get-started/benchmark-utility/) to submit the tracked experiments to the [openrlbenchmark/cleanrl](https://wandb.ai/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.