Feature discovery in reinforcement learning using genetic programming

Sertan Girgin 1 Philippe Preux 1, 2, 3
1 SEQUEL - Sequential Learning
LIFL - Laboratoire d'Informatique Fondamentale de Lille, Inria Lille - Nord Europe, LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal
Abstract : The goal of reinforcement learning is to find a policy, directly or indirectly through a value function, that maximizes the expected re- ward accumulated by an agent over time based on its interactions with the environment; a function of the state has to be learned. In some prob- lems, it may not be feasible, or even possible, to use the state variables as they are. Instead, a set of features are computed and used as in- put. However, finding a "good" set of features is generally a tedious task which requires a good domain knowledge. In this paper, we propose a ge- netic programming based approach for feature discovery in reinforcement learning. A population of individuals each representing possibly different number of candidate features is evolved, and feature sets are evaluated by their average performance on short learning trials. The results of ex- periments conducted on several benchmark problems demonstrate that the resulting features allow the agent to learn better policies.
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Communication dans un congrès
11th European Conference on Genetic Programming (EUROGP), 2008, Naples, Italy. Springer, 4971, pp.218-229, 2008, LNCS. 〈http://link.springer.com/chapter/10.1007%2F978-3-540-78671-9_19〉
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Sertan Girgin, Philippe Preux. Feature discovery in reinforcement learning using genetic programming. 11th European Conference on Genetic Programming (EUROGP), 2008, Naples, Italy. Springer, 4971, pp.218-229, 2008, LNCS. 〈http://link.springer.com/chapter/10.1007%2F978-3-540-78671-9_19〉. 〈hal-00826056〉

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