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- Title
A distributed adaptive policy gradient method based on momentum for multi-agent reinforcement learning.
- Authors
Shi, Junru; Wang, Xin; Zhang, Mingchuan; Liu, Muhua; Zhu, Junlong; Wu, Qingtao
- 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 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.
- Publication
Complex & Intelligent Systems, 2024, Vol 10, Issue 5, p7297
- ISSN
2199-4536
- Publication type
Article
- DOI
10.1007/s40747-024-01529-6