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Multi-agent Reinforcement Learning in Stochastic Single and Multi-stage Games

  • Katja Verbeeck
  • Ann Nowé
  • Maarten Peeters
  • Karl Tuyls
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3394)

Abstract

In this paper we report on a solution method for one of the most challenging problems in Multi-agent Reinforcement Learning, i.e. coordination. In previous work we reported on a new coordinated exploration technique for individual reinforcement learners, called Exploring Selfish Reinforcement Rearning (ESRL). With this technique, agents may exclude one or more actions from their private action space, so as to coordinate their exploration in a shrinking joint action space. Recently we adapted our solution mechanism to work in tree structured common interest multi-stage games. This paper is a roundup on the results for stochastic single and multi-stage common interest games.

Keywords

Nash Equilibrium Joint Action Multiagent System Equilibrium Path Learning Automaton 
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 2005

Authors and Affiliations

  • Katja Verbeeck
    • 1
  • Ann Nowé
    • 1
  • Maarten Peeters
    • 1
  • Karl Tuyls
    • 2
  1. 1.Computational Modeling LabVrije Universiteit BrusselBelgium
  2. 2.Theoretical Computer Science GroupUniversity of LimburgBelgium

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