QL2, a simple reinforcement learning scheme for two-player zero-sum Markov games

نویسندگان

  • Benoît Frénay
  • Marco Saerens
چکیده

Markov games are a framework which formalises n-agent reinforcement learning. For instance, Littman proposed the minimax-Q algorithm to model two-agent zero-sum problems. This paper proposes a new simple algorithm in this framework, QL2, and compares it to several standard algorithms (Q-learning, Minimax and minimax-Q). Experiments show that QL2 converges to optimal mixed policies, as minimax-Q, while using a surprisingly simple and cheap gradient-based updating rule.

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عنوان ژورنال:
  • Neurocomputing

دوره 72  شماره 

صفحات  -

تاریخ انتشار 2008