Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning

We study a multi-agent reinforcement learning (MARL) problem where the agents interact over a given network. The goal of the agents is to cooperatively maximize the average of their entropy-regularized long-term rewards. To overcome the curse of dimensionality and to reduce communication, we propose...

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Veröffentlicht in:	Performance evaluation review 2023-06, Vol.51 (1), p.83-84
Hauptverfasser:	Zhang, Yizhou, Qu, Guannan, Xu, Pan, Lin, Yiheng, Chen, Zaiwei, Wierman, Adam
Format:	Artikel
Sprache:	eng
Schlagworte:	Design and analysis of algorithms Mathematics of computing Network algorithms Networks Probability and statistics Theory of computation
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