A distributed adaptive policy gradient method based on momentum for multi-agent reinforcement learning

Autor: Junru Shi, Xin Wang, Mingchuan Zhang, Muhua Liu, Junlong Zhu, Qingtao Wu
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Complex & Intelligent Systems, Vol 10, Iss 5, Pp 7297-7310 (2024)
Druh dokumentu: article
ISSN: 2199-4536
2198-6053
DOI: 10.1007/s40747-024-01529-6
Popis: Abstract Policy Gradient (PG) method is one of the most popular algorithms in Reinforcement Learning (RL). However, distributed adaptive variants of PG are rarely studied in multi-agent. For this reason, this paper proposes a distributed adaptive policy gradient algorithm (IS-DAPGM) incorporated with Adam-type updates and importance sampling technique. Furthermore, we also establish the theoretical convergence rate of $$\mathcal {O}(1/\sqrt{T})$$ O ( 1 / T ) , where T represents the number of iterations, it can match the convergence rate of the state-of-the-art centralized policy gradient methods. In addition, many experiments are conducted in a multi-agent environment, which is a modification on the basis of Particle world environment. By comparing with some other distributed PG methods and changing the number of agents, we verify the performance of IS-DAPGM is more efficient than the existing methods.
Databáze: Directory of Open Access Journals
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