Distributed Neural Policy Gradient Algorithm for Global Convergence of Networked Multiagent Reinforcement Learning

This article studies the networked multiagent reinforcement learning problem, where the objective of agents is to collaboratively maximize the discounted average cumulative rewards. Different from the existing methods that suffer from poor expression due to linear function approximation, we propose...

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Vydané v:	IEEE transactions on automatic control Ročník 70; číslo 11; s. 7109 - 7124
Hlavní autori:	Dai, Pengcheng, Mo, Yuanqiu, Yu, Wenwu, Ren, Wei
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.11.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Approximation algorithms Communication networks Convergence Distributed neural policy gradient algorithm Function approximation global convergence Linear functions Linear programming Machine learning Multiagent systems networked multiagent reinforcement learning (NMARL) Neural networks Parameters Reinforcement learning Reviews Scalability Training Vectors
ISSN:	0018-9286, 1558-2523
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