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Challenges in High-Dimensional Reinforcement Learning with Evolution Strategies

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Book cover Parallel Problem Solving from Nature – PPSN XV (PPSN 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11102))

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Abstract

Evolution Strategies (ESs) have recently become popular for training deep neural networks, in particular on reinforcement learning tasks, a special form of controller design. Compared to classic problems in continuous direct search, deep networks pose extremely high-dimensional optimization problems, with many thousands or even millions of variables. In addition, many control problems give rise to a stochastic fitness function. Considering the relevance of the application, we study the suitability of evolution strategies for high-dimensional, stochastic problems. Our results give insights into which algorithmic mechanisms of modern ES are of value for the class of problems at hand, and they reveal principled limitations of the approach. They are in line with our theoretical understanding of ESs. We show that combining ESs that offer reduced internal algorithm cost with uncertainty handling techniques yields promising methods for this class of problems.

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Notes

  1. 1.

    https://github.com/NiMlr/High-Dim-ES-RL.

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Authors and Affiliations

  1. Institut für Neuroinformatik, Ruhr-Universität Bochum, Bochum, Germany

    Nils Müller & Tobias Glasmachers

Authors
  1. Nils Müller
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  2. Tobias Glasmachers
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Corresponding author

Correspondence to Tobias Glasmachers .

Editor information

Editors and Affiliations

  1. Inria Saclay, Palaiseau, France

    Anne Auger

  2. University of Coimbra, Coimbra, Portugal

    Carlos M. Fonseca

  3. University of Coimbra, Coimbra, Portugal

    Nuno Lourenço

  4. University of Coimbra, Coimbra, Portugal

    Penousal Machado

  5. University of Coimbra, Coimbra, Portugal

    Luís Paquete

  6. Colorado State University, Fort Collins, Colorado, USA

    Darrell Whitley

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Müller, N., Glasmachers, T. (2018). Challenges in High-Dimensional Reinforcement Learning with Evolution Strategies. In: Auger, A., Fonseca, C., Lourenço, N., Machado, P., Paquete, L., Whitley, D. (eds) Parallel Problem Solving from Nature – PPSN XV. PPSN 2018. Lecture Notes in Computer Science(), vol 11102. Springer, Cham. https://doi.org/10.1007/978-3-319-99259-4_33

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  • DOI: https://doi.org/10.1007/978-3-319-99259-4_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-99258-7

  • Online ISBN: 978-3-319-99259-4

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