Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm

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Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm (1998)

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by Junling Hu , Michael P. Wellman

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@MISC{Hu98multiagentreinforcement,
    author = {Junling Hu and Michael P. Wellman},
    title = {Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm},
    year = {1998}
}

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Abstract

In this paper, we adopt general-sum stochastic games as a framework for multiagent reinforcement learning. Our work extends previous work by Littman on zero-sum stochastic games to a broader framework. We design a multiagent Q-learning method under this framework, and prove that it converges to a Nash equilibrium under specified conditions. This algorithm is useful for finding 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 find optimal strategies.

Keyphrases

multiagent reinforcement learning theoretical framework optimal strategy nash equilibrium specified condition general-sum stochastic game multiagent q-learning method previous work zero-sum stochastic game unique nash equilibrium multiple nash equilibrium

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