Distributed Multi-Agent Reinforcement Learning by Actor-Critic Method

被引：10
|
作者：
Heredia, Paulo C. [1 ]
Mou, Shaoshuai [1 ]
机构：
[1] Purdue Univ, W Lafayette, IN 47906 USA
来源：
IFAC PAPERSONLINE | 2019年 / 52卷 / 20期
关键词：
D O I：
10.1016/j.ifacol.2019.12.182
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
We investigate the problem of multi-agent reinforcement learning, in which each agent only has access to its local reward and can only communicate with its nearby neighbors. A distributed algorithm based on actor-critic method has been developed to enable all agents to cooperatively learn a control policy that maximizes the global objective function. Simulations are also provided to validate the proposed algorithm. Copyright (C) 2019. The Authors. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码：363 / 368
页数：6
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