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.
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.
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.
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.
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.