Multiagent Hierarchical Reinforcement Learning With Asynchronous Termination Applied to Robotic Pick and Place

被引：1

作者：

Lan, Xi ^{[1
]}

Qiao, Yuansong ^{[1
]}

Lee, Brian ^{[1
]}

机构：

[1] Technol Univ Shannon, Software Res Inst, Athlone N37 HD68, Ireland

来源：

IEEE ACCESS | 2024年 / 12卷

基金：

爱尔兰科学基金会;

关键词：

Multi-agent system; pick and place; multi-agent-hierarchical reinforcement learning; multi-robot system; asynchronous termination; MULTIROBOT COORDINATION;

D O I：

10.1109/ACCESS.2024.3409076

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Recent breakthroughs in hierarchical multi-agent deep reinforcement learning (HMADRL) are propelling the development of sophisticated multi-robot systems, particularly in the realm of complex coordination tasks. These advancements hold significant potential for addressing the intricate challenges inherent in fast-evolving sectors such as intelligent manufacturing. In this study, we introduce an innovative simulator tailored for a multi-robot pick-and-place (PnP) operation, built upon the OpenAI Gym framework. Our aim is to demonstrate the efficacy of HMADRL algorithms for multi robot coordination in a manufacturing setting, concentrating on their influence on the gripping rate, a crucial indicator for gauging system performance and operational efficiency.

引用

页码：78988 / 79002

页数：15

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