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Towards Well-Defined Multi-agent Reinforcement Learning

  • Rinat Khoussainov
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3192)

Abstract

Multi-agent reinforcement learning (MARL) is an emerging area of research. However, it lacks two important elements: a coherent view on MARL, and a well-defined problem objective. We demonstrate these points by introducing three phenomena, social norms, teaching, and bounded rationality, which are inadequately addressed by the previous research. Based on the ideas of bounded rationality, we define a very broad class of MARL problems that are equivalent to learning in partially observable Markov decision processes (POMDPs). We show that this perspective on MARL accounts for the three missing phenomena, but also provides a well-defined objective for a learner, since POMDPs have a well-defined notion of optimality. We illustrate the concept in an empirical study, and discuss its implications for future research.

Keywords

Nash Equilibrium Stochastic Game Repeated Game Bounded Rationality Folk Theorem 
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 2004

Authors and Affiliations

  • Rinat Khoussainov
    • 1
  1. 1.Department of Computer ScienceUniversity College DublinBelfield, Dublin 4Ireland

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