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Opponent Aware Reinforcement Learning

Víctor Gallego,Roi Naveiro,D. Insua,D. Gómez‐Ullate

Published 2019 in arXiv.org

ABSTRACT

We introduce Threatened Markov Decision Processes (TMDPs) as an extension of the classical Markov Decision Process framework for Reinforcement Learning (RL). TMDPs allow suporting a decision maker against potential opponents in a RL context. We also propose a level-k thinking scheme resulting in a novel learning approach to deal with TMDPs. After introducing our framework and deriving theoretical results, relevant empirical evidence is given via extensive experiments, showing the benefits of accounting for adversaries in RL while the agent learns

PUBLICATION RECORD

  • Publication year

    2019

  • Venue

    arXiv.org

  • Publication date

    2019-08-22

  • Fields of study

    Mathematics, Computer Science

  • Identifiers
    arXiv 1908.08773
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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