Multi-agent collaborative exploration through graph-based deep reinforcement learning

Autonomous exploration by a single or multiple agents in an unknown environment leads to various applications in automation, such as cleaning, search and rescue, etc. Traditional methods normally take frontier locations and segmented regions of the environment into account to efficiently allocate ta...

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Main Authors: LUO, Tianze, SUBAGDJA, Budhitama, TAN, Ah-hwee, TAN, Ah-Hwee
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Language:English
Published: Institutional Knowledge at Singapore Management University 2019
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Online Access:https://ink.library.smu.edu.sg/sis_research/6173
https://ink.library.smu.edu.sg/context/sis_research/article/7176/viewcontent/Full_27_PID6151059.pdf
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spelling sg-smu-ink.sis_research-71762021-09-29T10:25:11Z Multi-agent collaborative exploration through graph-based deep reinforcement learning LUO, Tianze SUBAGDJA, Budhitama TAN, Ah-hwee TAN, Ah-Hwee Autonomous exploration by a single or multiple agents in an unknown environment leads to various applications in automation, such as cleaning, search and rescue, etc. Traditional methods normally take frontier locations and segmented regions of the environment into account to efficiently allocate target locations to different agents to visit. They may employ ad hoc solutions to allocate the task to the agents, but the allocation may not be efficient. In the literature, few studies focused on enhancing the traditional methods by applying machine learning models for agent performance improvement. In this paper, we propose a graph-based deep reinforcement learning approach to effectively perform multi-agent exploration. Specifically, we first design a hierarchical map segmentation method to transform the environment exploration problem to the graph domain, wherein each node of the graph corresponds to a segmented region in the environment and each edge indicates the distance between two nodes. Subsequently, based on the graph structure, we apply a Graph Convolutional Network (GCN) to allocate the exploration target to each agent. Our experiments show that our proposed model significantly improves the efficiency of map explorations across varying sizes of collaborative agents over the traditional methods. 2019-10-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/6173 info:doi/10.1109/AGENTS.2019.8929168 https://ink.library.smu.edu.sg/context/sis_research/article/7176/viewcontent/Full_27_PID6151059.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Graph convolutional networks Multi-agent map exploration Multi-robot system Reinforcement learning Artificial Intelligence and Robotics Databases and Information Systems
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Graph convolutional networks
Multi-agent map exploration
Multi-robot system
Reinforcement learning
Artificial Intelligence and Robotics
Databases and Information Systems
spellingShingle Graph convolutional networks
Multi-agent map exploration
Multi-robot system
Reinforcement learning
Artificial Intelligence and Robotics
Databases and Information Systems
LUO, Tianze
SUBAGDJA, Budhitama
TAN, Ah-hwee
TAN, Ah-Hwee
Multi-agent collaborative exploration through graph-based deep reinforcement learning
description Autonomous exploration by a single or multiple agents in an unknown environment leads to various applications in automation, such as cleaning, search and rescue, etc. Traditional methods normally take frontier locations and segmented regions of the environment into account to efficiently allocate target locations to different agents to visit. They may employ ad hoc solutions to allocate the task to the agents, but the allocation may not be efficient. In the literature, few studies focused on enhancing the traditional methods by applying machine learning models for agent performance improvement. In this paper, we propose a graph-based deep reinforcement learning approach to effectively perform multi-agent exploration. Specifically, we first design a hierarchical map segmentation method to transform the environment exploration problem to the graph domain, wherein each node of the graph corresponds to a segmented region in the environment and each edge indicates the distance between two nodes. Subsequently, based on the graph structure, we apply a Graph Convolutional Network (GCN) to allocate the exploration target to each agent. Our experiments show that our proposed model significantly improves the efficiency of map explorations across varying sizes of collaborative agents over the traditional methods.
format text
author LUO, Tianze
SUBAGDJA, Budhitama
TAN, Ah-hwee
TAN, Ah-Hwee
author_facet LUO, Tianze
SUBAGDJA, Budhitama
TAN, Ah-hwee
TAN, Ah-Hwee
author_sort LUO, Tianze
title Multi-agent collaborative exploration through graph-based deep reinforcement learning
title_short Multi-agent collaborative exploration through graph-based deep reinforcement learning
title_full Multi-agent collaborative exploration through graph-based deep reinforcement learning
title_fullStr Multi-agent collaborative exploration through graph-based deep reinforcement learning
title_full_unstemmed Multi-agent collaborative exploration through graph-based deep reinforcement learning
title_sort multi-agent collaborative exploration through graph-based deep reinforcement learning
publisher Institutional Knowledge at Singapore Management University
publishDate 2019
url https://ink.library.smu.edu.sg/sis_research/6173
https://ink.library.smu.edu.sg/context/sis_research/article/7176/viewcontent/Full_27_PID6151059.pdf
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