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Evolving Genes to Balance a Pole

Miguel Nicolau 1, 2 Marc Schoenauer 1, 2 W. Banzhaf 3
1 TAO - Machine Learning and Optimisation
CNRS - Centre National de la Recherche Scientifique : UMR8623, Inria Saclay - Ile de France, UP11 - Université Paris-Sud - Paris 11, LRI - Laboratoire de Recherche en Informatique
2 LRI - Laboratoire de Recherche en Informatique
3 Department of Computer Science - St. John's
Abstract : We discuss how to use a Genetic Regulatory Network as an evolutionary representation to solve a typical GP reinforcement problem, the pole balancing. The network is a modified version of an Artificial Regulatory Network proposed a few years ago, and the task could be solved only by finding a proper way of connecting inputs and outputs to the network. We show that the representation is able to generalize well over the problem domain, and discuss the performance of different models of this kind.
Keywords : Genetic Regulatory Networks reinforcement learning
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Submitted on : Sunday, May 16, 2010 - 2:23:13 PM
Last modification on : Thursday, July 8, 2021 - 3:48:44 AM
Long-term archiving on: : Thursday, September 16, 2010 - 2:06:50 PM

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  • HAL Id : inria-00483681, version 1
  • ARXIV : 1005.2815

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CNRS | INRIA | UMR8623 | LRI-AO | EC-PARIS

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Miguel Nicolau, Marc Schoenauer, W. Banzhaf. Evolving Genes to Balance a Pole. European Conference on Genetic Programming, Apr 2010, Istanbul, Turkey. pp.196-207. ⟨inria-00483681⟩

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