A near-optimal poly-time algorithm for learning in a class of stochastic games

Ronen I. Brafman, Moshe Tennenholtz

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations

Abstract

We present a new algorithm for polynomial time learning of near optimal behavior in stochastic games. This algorithm incorporates and integrates important recent results of Kearns and Singh [1998] in reinforcement learning and of Monderer and Tennenholtz [1997] in repeated games. In stochastic games we face an exploration vs. exploitation dilemma more complex than in Markov decision processes. Namely, given information about particular parts of a game matrix, how much effort should the agent invest in learning its unknown parts. We explain and address these issues within the class of single controller stochastic games. This solution can be extended to stochastic games in general.

Original languageEnglish
Pages (from-to)734-739
Number of pages6
JournalIJCAI International Joint Conference on Artificial Intelligence
Volume2
StatePublished - 1 Dec 1999
Event16th International Joint Conference on Artificial Intelligence, IJCAI 1999 - Stockholm, Sweden
Duration: 31 Jul 19996 Aug 1999

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