# Description

Currently, the
[PreTrainedPolicyAction](https://github.com/isaac-sim/IsaacLab/blob/v1.4.0/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/manager_based/navigation/mdp/pre_trained_policy_action.py#L24)
class does not reset the actions in the low-level observations when a
new episode starts.

In my custom legged robot navigation task, the behavior was correct only
during the first training episode but failed from the second episode
onward. At the start of a new episode, the action observations are not
reset and retain the last actions from the previous episode. This can
impact training, as in my case, where the actions at the end of an
episode differ significantly from those required at the beginning of an
episode.

This PR resolves the issue by resetting the low-level action
observations at the beginning of each new episode.


## Type of change

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applicable. -->

- Bug fix (non-breaking change which fixes an issue)


## 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
- [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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