jaisalab 0.0.1

Framework that builds on the garage toolkit for the evaluation and development of constrained RL algorithms.

jaisalab: A Toolkit for Safe RL

jaisalab is a framework for developing and evaluating reinforcement learning algorithms that take into account constraints that should be satisfied in OpenAI gym environments, that also provides PyTorch implementations for a few state-of-the-art algorithms in Constrained RL. Namely, the algorithms implemented are Constrained Policy Optimization (CPO) <https://github.com/jaimesabalimperial/jaisalab/blob/master/jaisalab/algos/cpo.py>, the Safety Augmented RL (SAUTE) <https://github.com/jaimesabalimperial/jaisalab/blob/master/jaisalab/envs/saute_env.py> wrapper that incorporates safety to any RL algorithm in a plug-n-play manner, and Distributional Constrained Policy Optimization, a modification to CPO that exploits uncertainty in the cost value function to better satisfy constraints.

The framework builds on the garage <https://github.com/rlworkgroup/garage>_ toolkit to add functionality to safe RL settings (i.e. where there is a cost associated with each state-action pair on top of the reward) and thus provides an auxiliary set of modular tools for implementing constrained RL algorithms, analogously to how garage provides these for regular RL settings.

Running Unit and Integration Tests

This project uses pytest for unit and integration testing (included in the developer dependencies). The tests may be run from the root directory as follows:

.. code-block:: console

$ pytest
...
===== x passed, x warnings in x.xx seconds =====

Instructions for Experiment Reproduction

The final results for DCPO, Saute TRPO, Vanilla TRPO, and CPO showed in the final report are equivalent to the experiments in jaisalab.experiments.backlog <https://github.com/jaimesabalimperial/jaisalab/blob/master/jaisalab/experiments/backlog.py>_, ran across seeds 1-10. The evaluation of the trained algorithms is done through the SeedEvaluator object for 30 epochs and the plots are made using the methods of the RLPlotter object.

References

The code implementations for the algorithms present in jaisalab are based partly or fully on the repositories of their respective papers. For CPO, the original TensorFlow implementation can be found in here <https://github.com/jachiam/cpo>, but this had to be translated to PyTorch to fit our framework. For SAUTE, the implementation was much more straightforward since it's simply a wrapper around the environment, rather than a separate algorithm that should fit our framework as a whole, so the code present in jaisalab/envs/safe_env and jaisalab/envs/saute_env was taken directly from Huawei's repository <https://github.com/huawei-noah/HEBO> (more specifically here <https://github.com/huawei-noah/HEBO/tree/405dc4ceb93a79f0d1f0eaa24f5458dd26de1d05/SAUTE/envs/wrappers>_).

Citing jaisalab

If you use jaisalab for academic research, please cite the repository using the following BibTeX entry. You should update the commit field with the commit or release tag your publication uses.

.. code-block:: console

@misc{jaisalab,
author = {Jaime Sabal Bermúdez},
title = {jaisalab: A garage-based framework for reproducible constrained reinforcement learning research},
year = {2022},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/jaimesabalimperial/jaisalab}},
commit = {cc5bc6b4dc7074af1f47d21c6d312429b2ccb931}

Notes

Developed in partial fulfillment of the individual project of Jaime Sabal Bermúdez for the MSc degree in Artificial Intelligence of Imperial College London.

I would like to thank Dr. Calvin Tsay for his guidance and advice throughout the project.

Disclaimer: While jaisalab is an open-source project and contributions are welcome, we cannot guarantee that the codebase will be actively maintained in the future.