README.md

Enhancing Reinforcement Learning agents with Local Guides

This is the official implementation of the techniques discussed in the paper Enhancing Reinforcement Learning agents with Local Guides.

Create the conda virtual environment

conda create --name rllg python=3.8
conda activate rllg
pip install -e .

Steps to launch it for a new environment

• In the folder envs, create a folder with the name of the environment with 3 files:
  • create_env_name to create the environment
  • local_expert_policy for the local expert
  • confidence for the confidence function $\lambda$
• Add the environment in the global files creation and confidence in envs
• Add a config file in ray_config
• Modify the main file to include the new environment
• Enjoy :)

Notes regarding the Point-Reach environment

PointReach is based on Bullet-Safety-Gym, and has been modified internally (directly in their source code) to make it more difficult.

All the details can be found in Appendix B of the paper.

Visualization

All the results are saved in a ray tune Experimentanalysis. You can plot them in the Visualization.ipynb notebook.

License

We follow MIT License. Please see the License file for more information.

Disclaimer: This is not an officially supported Huawei Product.

Credits

This code is built upon the SimpleSAC Github, and some wrappers of gym.

Cite us

If you find this technique useful and you use it in a project, please cite it:

@inproceedings{daoudi2023enhancing,
  title={Enhancing Reinforcement Learning Agents with Local Guides},
  author={Daoudi, Paul and Robu, Bogdan and Prieur, Christophe and Dos Santos, Ludovic and Barlier, Merwan},
  booktitle={Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems},
  pages={829--838},
  year={2023}
}