- author = "Mathurin Videau and Alessandro Leite and Olivier Teytaud and Marc Schoenauer",
- title = "Multi-Objective Genetic Programming for Explainable Reinforcement Learning",
- booktitle = "EuroGP 2022: Proceedings of the 25th European Conference on Genetic Programming",
- year = "2022",
- editor = "Eric Medvet and Gisele Pappa and Bing Xue",
- series = "LNCS",
- volume = "13223",
- publisher = "Springer Verlag",
- address = "Madrid, Spain",
- pages = "278--293",
- month = "20-22 " # apr,
- organisation = "EvoStar, Species",
- keywords = "genetic algorithms, genetic programming, Reinforcement Learning, Explainable Reinforcement Learning (XRL), Genetic Programming Reinforcement Learning (GPRL): Poster",
- isbn13 = "978-3-031-02055-1",
-
URL = "
https://universite-paris-saclay.hal.science/hal-03886307/",
-
DOI = "
10.1007/978-3-031-02056-8_18",
- abstract = "Deep reinforcement learning has met noticeable successes recently for a wide range of control problems. However, this is typically based on thousands of weights and non-linearities, making solutions complex, not easily reproducible, uninterpretable and heavy. The present paper presents genetic programming approaches for building symbolic controllers. Results are competitive, in particular in the case of delayed rewards, and the solutions are lighter by orders of magnitude and much more understandable.",
-
notes = "M. Videau: Now at Meta AI
Research.
http://www.evostar.org/2022/eurogp/ Part of \cite{Medvet:2022:GP} EuroGP'2022 held inconjunction with EvoApplications2022 EvoCOP2022 EvoMusArt2022",
