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TeachMyAgent: a Benchmark for Automatic Curriculum Learning in Deep RL

TeachMyAgent is a testbed platform for Automatic Curriculum Learning methods. We leverage Box2D procedurally generated environments to assess the performance of teacher algorithms in continuous task spaces. Our repository provides:

  • Two parametric Box2D environments: Stumps Tracks and Parkour
  • Multiple embodiments with different locomotion skills (e.g. bipedal walker, spider, climbing chimpanzee, fish)
  • Two Deep RL students: SAC and PPO
  • Several ACL algorithms: ADR, ALP-GMM, Covar-GMM, SPDL, GoalGAN, Setter-Solver, RIAC
  • Two benchmark experiments using elements above: Skill-specific comparison and global performance assessment
  • Three notebooks for systematic analysis of results using statistical tests along with visualization tools (plots, videos...) allowing to reproduce our figures

See our documentation for an exhaustive list.

global_schema

Using this, we performed a benchmark of the previously mentioned ACL methods which can be seen in our paper. We also provide additional visualization on our website.

Installation

1- Get the repository

git clone https://github.com/flowersteam/TeachMyAgent
cd TeachMyAgent/

2- Install it, using Conda for example (use Python >= 3.6)

conda create --name teachMyAgent python=3.6
conda activate teachMyAgent
pip install -e .

Note: For Windows users, add -f https://download.pytorch.org/whl/torch_stable.html to the pip install -e . command.

Import baseline results from our paper

In order to benchmark methods against the ones we evaluated in our paper you must download our results:

  1. Go to the notebooks folder
  2. Make the download_baselines.sh script executable: chmod +x download_baselines.sh
  3. Download results: ./download_baselines.sh

WARNING: This will download a zip weighting approximayely 4.5GB. Then, our script will extract the zip file in TeachMyAgent/data. Once extracted, results will weight approximately 15GB.

Usage

See our documentation for details on how to use our platform to benchmark ACL methods.

Development

See CONTRIBUTING.md for details.

Citing

If you use TeachMyAgent in your work, please cite the accompanying paper:

@inproceedings{romac2021teachmyagent,
  author    = {Cl{\'{e}}ment Romac and
               R{\'{e}}my Portelas and
               Katja Hofmann and
               Pierre{-}Yves Oudeyer},
  title     = {TeachMyAgent: a Benchmark for Automatic Curriculum Learning in Deep
               {RL}},
  booktitle = {Proceedings of the 38th International Conference on Machine Learning,
               {ICML} 2021, 18-24 July 2021, Virtual Event},
  series    = {Proceedings of Machine Learning Research},
  volume    = {139},
  pages     = {9052--9063},
  publisher = {{PMLR}},
  year      = {2021}
}