Multi-agent Coordination using Reinforcement Learning with a Relay Agent

被引：1

作者：

Zemzem, Wiem ^{[1
]}

Tagina, Moncef ^{[1
]}

机构：

[1] Univ Manouba, Natl Sch Comp Sci, COSMOS Lab, Tunis, Tunisia

来源：

ICEIS: PROCEEDINGS OF THE 19TH INTERNATIONAL CONFERENCE ON ENTERPRISE INFORMATION SYSTEMS - VOL 1 | 2017年

关键词：

Distributed Reinforcement Learning; A Cooperative Action Selection Strategy; A Relay Agent; Unknown and Stationary Environments;

D O I：

10.5220/0006327305370545

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper focuses on distributed reinforcement learning in cooperative multi-agent systems, where several simultaneously and independently acting agents have to perform a common foraging task. To do that, a novel cooperative action selection strategy and a new kind of agents, called "relay agent", are proposed. The conducted simulation tests indicate that our proposals improve coordination between learners and are extremely efficient in terms of cooperation in large, unknown and stationary environments.

引用

页码：537 / 545

页数：9

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