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ATAL
2009
Springer

Multiagent reinforcement learning: algorithm converging to Nash equilibrium in general-sum discounted stochastic games

9 years 4 months ago
Multiagent reinforcement learning: algorithm converging to Nash equilibrium in general-sum discounted stochastic games
This paper introduces a multiagent reinforcement learning algorithm that converges with a given accuracy to stationary Nash equilibria in general-sum discounted stochastic games. Under some assumptions we formally prove its convergence to Nash equilibrium in self-play. We claim that it is the first algorithm that converges to stationary Nash equilibrium in the general case. Categories and Subject Descriptors I.2.6 [Artificial Intelligence]: Learning; I.2.11 [Artificial Intelligence]: Distributed Artificial Intelligence—Multiagent systems General Terms Algorithms, Theory Keywords algorithmic game theory, stochastic games, computation of equilibria, multiagent reinforcement learning
Natalia Akchurina
Added 26 May 2010
Updated 26 May 2010
Type Conference
Year 2009
Where ATAL
Authors Natalia Akchurina
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