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Mayank Mittal authored
# Description This PR adds the following managers similar to how we currently handle observation and reward terms. * **Termination Manager**: Iterates over all the configured terms and computes the done signals as an OR operator over each term's output. Additionally, `time_outs` are handled separately as they are optional (i.e. only used in fixed-length episodic learning). * **Randomization Manager**: Handles various randomization (such as resetting the state of the environments, and modifying various physics attributes). * **Curriculum Manager**: Iterates over all the configured terms and sets the curriculum setting into the environment accordingly. ## Type of change - New feature (non-breaking change which adds functionality) - This change requires a documentation update ## Checklist - [x] I have run the [`pre-commit` checks](https://pre-commit.com/) with `./orbit.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 --------- Co-authored-by:
David Hoeller <dhoeller@ethz.ch>
Co-authored-by:
Nikita Rudin <nrudin@nvidia.com>
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| .. | ||
| __init__.py | ||
| curriculum_manager.py | ||
| manager_base.py | ||
| manager_cfg.py | ||
| observation_manager.py | ||
| randomization_manager.py | ||
| reward_manager.py | ||
| termination_manager.py |