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- W1526551885 abstract "This paper proposes a multi-agent Q-learning algorithm called meta-game-Q learning that is developed from the meta-game equilibrium concept Different from Nash equilibrium, meta-game equilibrium can achieve the optimal joint action game through deliberating its preference and predicting others' policies in the general-sum game A distributed negotiation algorithm is used to solve the meta-game equilibrium problem instead of using centralized linear programming algorithms We use the repeated prisoner's dilemma example to empirically demonstrate that the algorithm converges to meta-game equilibrium." @default.
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- W1526551885 date "2004-01-01" @default.
- W1526551885 modified "2023-10-01" @default.
- W1526551885 title "Meta-game Equilibrium for Multi-agent Reinforcement Learning" @default.
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- W1526551885 doi "https://doi.org/10.1007/978-3-540-30549-1_81" @default.
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