# Description

This PR introduces the Population Based Training algorithm originally
implemented in

Petrenko, Aleksei, et al. "Dexpbt: Scaling up dexterous manipulation for
hand-arm systems with population based training." arXiv preprint
arXiv:2305.12127 (2023).

Pbt algorithm offers a alternative to scaling when increasing number of
environment has margin effect.
It takes idea in natural selection and stochastic property in
rl-training to always keeps the top performing agent while replace weak
agent with top performance to overcome the catastrophic failure, and
improve the exploration.

Training view, underperformers are rescued by best performers and later
surpasses them and become best performers
<img width="1078" height="509" alt="Screenshot from 2025-09-09 00-55-11"
src="https://github.com/user-attachments/assets/34434bf1-5cb6-4956-a344-49c9969d4861"
/>


Note:
PBT is still at beta phase and has below limitations:

1. in theory It can work with any rl algorithm but current
implementation only works for rl-games
2. The API could be furthur simplified without needing explicitly input
num_policies or policy_idx, which allows for dynamic max_population, but
it is for future work

## Screenshots

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## Checklist

- [x] I have run the [`pre-commit` checks](https://pre-commit.com/) with
`./isaaclab.sh --format`
- [x] I have made corresponding changes to the documentation
- [x] My changes generate no new warnings
- [ ] I have added tests that prove my fix is effective or that my
feature works
- [x] I have updated the changelog and the corresponding version in the
extension's `config/extension.toml` file
- [x] I have added my name to the `CONTRIBUTORS.md` or my name already
exists there

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