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  in reinforcement learning and of Monderer and Tennenholtz  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.
|Number of pages||6|
|Journal||IJCAI International Joint Conference on Artificial Intelligence|
|State||Published - 1 Dec 1999|
|Event||16th International Joint Conference on Artificial Intelligence, IJCAI 1999 - Stockholm, Sweden|
Duration: 31 Jul 1999 → 6 Aug 1999