A Study of Collaborative and Distributed Multi-agent Path-planning using Reinforcement Learning (강화학습 기반의 분산 다중 에이전트 협업 경로 탐색에 관한 연구)

Min-suk Kim (김민석)

doi:10.9708/jksci.2021.26.03.009

A Study of Collaborative and Distributed Multi-agent Path-planning using Reinforcement Learning

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2021, 26(3), pp.9~17
DOI : 10.9708/jksci.2021.26.03.009
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science
Received : February 10, 2021
Accepted : March 11, 2021
Published : March 31, 2021

Min-suk Kim ¹

¹상명대학교

Accredited

ABSTRACT

In this paper, an autonomous multi-agent path planning using reinforcement learning for monitoring of infrastructures and resources in a computationally distributed system was proposed. Reinforcement-learning-based multi-agent exploratory system in a distributed node enable to evaluate a cumulative reward every action and to provide the optimized knowledge for next available action repeatedly by learning process according to a learning policy. Here, the proposed methods were presented by (a) approach of dynamics-based motion constraints multi-agent path-planning to reduce smaller agent steps toward the given destination(goal), where these agents are able to geographically explore on the environment with initial random-trials versus optimal-trials, (b) approach using agent sub-goal selection to provide more efficient agent exploration(path-planning) to reach the final destination(goal), and (c) approach of reinforcement learning schemes by using the proposed autonomous and asynchronous triggering of agent exploratory phases.

KEYWORDS

Reinforcement Learning, Multi-agent, Sub-goal, Sharing Information, Collaborative

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* References for papers published after 2025 are currently being built.

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This paper was written with support from the National Research Foundation of Korea.

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Journal of The Korea Society of Computer and Information 2025 KCI Impact Factor : 1.01

A Study of Collaborative and Distributed Multi-agent Path-planning using Reinforcement Learning

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Journal of The Korea Society of Computer and Information 2025 KCI Impact Factor : 1.01

A Study of Collaborative and Distributed Multi-agent Path-planning using Reinforcement Learning

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