- author = "Robert Babuska",
- title = "Genetic programming methods for reinforcement learning",
- booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference",
- year = "2019",
- editor = "Manuel Lopez-Ibanez and Thomas Stuetzle and Anne Auger and Petr Posik and Leslie {Peprez Caceres} and Andrew M. Sutton and Nadarajen Veerapen and Christine Solnon and Andries Engelbrecht and Stephane Doncieux and Sebastian Risi and Penousal Machado and Vanessa Volz and Christian Blum and Francisco Chicano and Bing Xue and Jean-Baptiste Mouret and Arnaud Liefooghe and Jonathan Fieldsend and Jose Antonio Lozano and Dirk Arnold and Gabriela Ochoa and Tian-Li Yu and Holger Hoos and Yaochu Jin and Ting Hu and Miguel Nicolau and Robin Purshouse and Thomas Baeck and Justyna Petke and Giuliano Antoniol and Johannes Lengler and Per Kristian Lehre",
- pages = "2--2",
- address = "Prague, Czech Republic",
- publisher = "ACM",
- publisher_address = "New York, NY, USA",
- month = "13-17 " # jul,
- organisation = "SIGEVO",
- note = "Invited keynote",
- keywords = "genetic algorithms, genetic programming, Reinforcement learning, Symbolic regression",
- isbn13 = "978-1-4503-6111-8",
-
DOI = "
doi:10.1145/3321707.3326935",
- size = "1 page",
- abstract = "Reinforcement Learning (RL) algorithms can be used to optimally solve dynamic decision-making and control problems. With continuous valued state and input variables, RL algorithms must rely on function approximators to represent the value function and policy mappings. Commonly used numerical approximators, such as neural networks or basis function expansions, have two main drawbacks: they are black-box models offering no insight in the mappings learnt, and they require significant trial and error tuning of their meta-parameters. In addition, results obtained with deep neural networks suffer from the lack of reproducibility. In this talk, we discuss a family of new approaches to constructing smooth approximators for RL by means of genetic programming and more specifically by symbolic regression. We show how to construct process models and value functions represented by parsimonious analytic expressions using state-of-the-art algorithms, such as Single Node Genetic Programming and Multi-Gene Genetic Programming. We will include examples of non-linear control problems that can be successfully solved by reinforcement learning with symbolic regression and illustrate some of the challenges this exciting field of research is currently facing.",
-
notes = "Abstract of keynote.
Also known as \cite{3326935} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)",
