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- W4372346651 abstract "The graphical game provides an effective method for modeling different kinds of sparse interactions in multi-agent reinforcement learning. Most previous work on game abstraction lacks theoretical guarantees of convergence. In this paper, we adopt the ${mathcal{N}}$-step return signal to detect interactions between agents and build the Markov graphical game based on it. We analyze that the solution of the Markov graphical game is an ϵ-Nash equilibrium which guarantees the convergence of the proposed NSR-G <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2</sup> NashQ algorithm theoretically. Also, we have done experiments in different multi-agent reinforcement learning tasks with both tabular and function approximation solutions. The results show the NSR-G <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2</sup> NashQ algorithm accelerates the convergence of agents to the optimal policy." @default.
- W4372346651 created "2023-05-07" @default.
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- W4372346651 date "2023-06-04" @default.
- W4372346651 modified "2023-09-27" @default.
- W4372346651 title "Convergence Analysis of Graphical Game-Based Nash Q−Learning using the Interaction Detection Signal of N−Step Return" @default.
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- W4372346651 doi "https://doi.org/10.1109/icassp49357.2023.10095235" @default.
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