Multiagent Hierarchical Reinforcement Learning with Asynchronous Termination applied to Robotic Pick and Place

Xi Lan; Yuansong Qiao; Brian Lee

doi:10.1109/ACCESS.2024.3409076

Multiagent Hierarchical Reinforcement Learning with Asynchronous Termination applied to Robotic Pick and Place

Xi Lan, Yuansong Qiao, Brian Lee

Research and External Services: Midlands

Limerick Institute of Technology,

Research output: Contribution to journal › Article › peer-review

Abstract

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.

Original language	English
Pages (from-to)	78988-79002
Number of pages	15
Journal	IEEE Access
Volume	12
DOIs	https://doi.org/10.1109/ACCESS.2024.3409076
Publication status	Published - 2024

Keywords

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

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10.1109/ACCESS.2024.3409076

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title = "Multiagent Hierarchical Reinforcement Learning with Asynchronous Termination applied to Robotic Pick and Place",

abstract = "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.",

keywords = "Multi-agent system, asynchronous termination, multi-agent-hierarchical reinforcement learning, multi-robot system, pick and place",

author = "Xi Lan and Yuansong Qiao and Brian Lee",

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AB - 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.

KW - Multi-agent system

KW - asynchronous termination

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SP - 78988

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JO - IEEE Access

JF - IEEE Access

ER -

Multiagent Hierarchical Reinforcement Learning with Asynchronous Termination applied to Robotic Pick and Place

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Keywords

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