Supaero Reinforcement Learning Initiative
The software section of this site links to open source code developped during projects stemming from our research interests. We generally open source our code when we believe it has a value to the community. Consequently, if some research area seems to lack open source code, there probably is something cooking internally.
https://github.com/SuReLI/Deep-RL-agents
Our library of Deep RL agents. A great place to get started for most people who work with SuReLI (inside or outside collaborators).
Contributors: Valentin Guillet, Emmanuel Rachelson.
https://github.com/SuReLI/dyna-gym
This is a pip package implementing Reinforcement Learning algorithms in non-stationary environments supported by the OpenAI Gym toolkit.
Contributor: Erwan Lecarpentier.
A Gym-compatible acrobot environment with several reward functions corresponding to different tasks.
Contributor: Jean-Jacques Simeoni.
https://github.com/SuReLI/naibx-mlc
Naive Bayes Classification for Subset Selection (NaiBX): an extension of Naive Bayes to multi-label classification.
See also the related paper: Naive Bayes Classification for Subset Selection
Contributors: Luca Mossina, Emmanuel Rachelson.
Using Deep RL to avoid or recover from stall on an autonomous sail-boat’s wing.
Contributors: N. Megel, A. Bonet-Munoz, T. Karch, Y. Brière, E. Rachelson.
MCTS planning for the long-term path planning of an autonomous sail-boat.
Contributors: F. Brulport, J.-M. Belley, P. Barde, C. Chanel, Y. Brière, E. Rachelson.
https://github.com/SuReLI/L2F-sim
A stand-alone, C++ simulator of the flight dynamics of an autonomous glider within convective soaring conditions. Used to benchmark RL methods for control and planning of autonomous gliders.
Contributors: S. Rapp, M. Melo Oliver, R. Madelaine, L. Becq, A. Bufort, H. Akhmouch, T. Le Minh, E. Herlaut, N. El Jaafari, S. Ganapathi-Raju, E. Lecarpentier, E. Rachelson.