# Description

This PR introduces support for early stopping in Ray integration through
the `Stopper` class. It enables trials to end sooner when they are
unlikely to yield useful results, reducing wasted compute time and
speeding up experimentation.

Previously, when running hyperparameter tuning with Ray integration, all
trials would continue until the training configuration’s maximum
iterations were reached, even if a trial was clearly underperforming.
This wasn’t always efficient, since poor-performing trials could often
be identified early on. With this PR, an optional early stopping
mechanism is introduced, allowing Ray to terminate unpromising trials
sooner and improve the overall efficiency of hyperparameter tuning.

The PR also includes a `CartpoleEarlyStopper` example in
`vision_cartpole_cfg.py`. This serves as a reference implementation that
halts a trial if the `out_of_bounds` metric doesn’t reduce after a set
number of iterations. It’s meant as a usage example: users are
encouraged to create their own custom stoppers tailored to their
specific use cases.

Fixes #3270.

## Type of change

- New feature (non-breaking change which adds functionality)

## Checklist

- [x] I have run the [`pre-commit` checks](https://pre-commit.com/) with
`./isaaclab.sh --format`
- [ ] 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
- [ ] 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

---------
Co-authored-by: garylvov <67614381+garylvov@users.noreply.github.com>
Co-authored-by: garylvov <gary.lvov@gmail.com>
Co-authored-by: sbtc-sipbb <sbtc@sipbb.ch>
Co-authored-by: Kelly Guo <kellyg@nvidia.com>