Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm
نویسندگان
چکیده
In this paper we adopt general sum stochas tic games as a framework for multiagent re inforcement learning Our work extends pre vious work by Littman on zero sum stochas tic games to a broader framework We de sign a multiagent Q learning method under this framework and prove that it converges to a Nash equilibrium under speci ed condi tions This algorithm is useful for nding the optimal strategy when there exists a unique Nash equilibrium in the game When there exist multiple Nash equilibria in the game this algorithm should be combined with other learning techniques to nd optimal strategies
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