Distributed Actor-Critic Algorithms for Multiagent Reinforcement Learning Over Directed Graphs

Actor-critic (AC) cooperative multiagent reinforcement learning (MARL) over directed graphs is studied in this article. The goal of the agents in MARL is to maximize the globally averaged return in a distributed way, i.e., each agent can only exchange information with its neighboring agents. AC meth...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems Jg. 34; H. 10; S. 7210 - 7221
Hauptverfasser:	Dai, Pengcheng, Yu, Wenwu, Wang, He, Baldi, Simone
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.10.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Approximation algorithms Convergence Directed graph Directed graphs distributed actor–critic (AC) algorithm Function approximation Graph theory Graphs Linear functions Marl Matrices (mathematics) multiagent reinforcement learning (MARL) Multiagent systems Protocols push-sum protocol Q-learning Reinforcement Stochasticity Topology Weight
ISSN:	2162-237X, 2162-2388, 2162-2388
Online-Zugang:	Volltext
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