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Efficient Non-linear Control Through Neuroevolution

  • Faustino Gomez
  • Jürgen Schmidhuber
  • Risto Miikkulainen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4212)

Abstract

Many complex control problems are not amenable to traditional controller design. Not only is it difficult to model real systems, but often it is unclear what kind of behavior is required. Reinforcement learning (RL) has made progress through direct interaction with the task environment, but it has been difficult to scale it up to large and partially observable state spaces. In recent years, neuroevolution, the artificial evolution of neural networks, has shown promise in tasks with these two properties. This paper introduces a novel neuroevolution method called CoSyNE that evolves networks at the level of weights. In the most extensive comparison of RL methods to date, it was tested in difficult versions of the pole-balancing problem that involve large state spaces and hidden state. CoSyNE was found to be significantly more efficient and powerful than the other methods on these tasks, forming a promising foundation for solving challenging real-world control tasks.

Keywords

Reinforcement Learning Synaptic Weight Cerebellar Model Articulation Controller Covariance Matrix Adaptation Evolutionary Strategy Large State Space 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Faustino Gomez
    • 1
  • Jürgen Schmidhuber
    • 1
    • 2
  • Risto Miikkulainen
    • 3
  1. 1.Dalle Molle Institute for Artificial Intelligence (IDSIA)Lugano
  2. 2.Technische Universität MünchenGarchingGermany
  3. 3.Department of Computer SciencesUniversity of TexasAustinUSA

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