Scaling up cooperative multi-agent reinforcement learning systems

Cooperative multi-agent reinforcement learning methods aim to learn effective collaborative behaviours of multiple agents performing complex tasks. However, existing MARL methods are commonly proposed for fairly small-scale multi-agent benchmark problems, wherein both the number of agents and the le...

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Main Author:	GENG, Minghong
Format:	text
Language:	English
Published:	Institutional Knowledge at Singapore Management University 2024
Subjects:	Multi-agent Reinforcement Learning Scaling up MARL Long-horizon MARL Hierarchical Multi-agent Systems Task Decomposition Multi-agent learning Reinforcement learning Scalability Collective learning Databases and Information Systems
Online Access:	https://ink.library.smu.edu.sg/sis_research/9750 https://ink.library.smu.edu.sg/context/sis_research/article/10750/viewcontent/p2737__1_.pdf
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Institution:	Singapore Management University
Language:	English

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spelling	sg-smu-ink.sis_research-107502024-12-16T03:21:23Z Scaling up cooperative multi-agent reinforcement learning systems GENG, Minghong Cooperative multi-agent reinforcement learning methods aim to learn effective collaborative behaviours of multiple agents performing complex tasks. However, existing MARL methods are commonly proposed for fairly small-scale multi-agent benchmark problems, wherein both the number of agents and the length of the time horizons are typically restricted. My initial work investigates hierarchical controls of multi-agent systems, where a unified overarching framework coordinates multiple smaller multi-agent subsystems, tackling complex, long-horizon tasks that involve multiple objectives. Addressing another critical need in the field, my research introduces a comprehensive benchmark for evaluating MARL methods in long-horizon, multi-agent, and multi-objective scenarios. This benchmark aims to fill the current gap in the MARL community for assessing methodologies in more complex and realistic scenarios. My dissertation would focus on proposing and evaluating methods for scaling up multi-agent systems in two aspects: structural-wise increasing the number of reinforcement learning agents and temporal-wise extending the planning horizon and complexity of problem domains that agents are deployed in. 2024-05-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/9750 https://ink.library.smu.edu.sg/context/sis_research/article/10750/viewcontent/p2737__1_.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 Multi-agent Reinforcement Learning Scaling up MARL Long-horizon MARL Hierarchical Multi-agent Systems Task Decomposition Multi-agent learning Reinforcement learning Scalability Collective learning 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	Multi-agent Reinforcement Learning Scaling up MARL Long-horizon MARL Hierarchical Multi-agent Systems Task Decomposition Multi-agent learning Reinforcement learning Scalability Collective learning Databases and Information Systems
spellingShingle	Multi-agent Reinforcement Learning Scaling up MARL Long-horizon MARL Hierarchical Multi-agent Systems Task Decomposition Multi-agent learning Reinforcement learning Scalability Collective learning Databases and Information Systems GENG, Minghong Scaling up cooperative multi-agent reinforcement learning systems
description	Cooperative multi-agent reinforcement learning methods aim to learn effective collaborative behaviours of multiple agents performing complex tasks. However, existing MARL methods are commonly proposed for fairly small-scale multi-agent benchmark problems, wherein both the number of agents and the length of the time horizons are typically restricted. My initial work investigates hierarchical controls of multi-agent systems, where a unified overarching framework coordinates multiple smaller multi-agent subsystems, tackling complex, long-horizon tasks that involve multiple objectives. Addressing another critical need in the field, my research introduces a comprehensive benchmark for evaluating MARL methods in long-horizon, multi-agent, and multi-objective scenarios. This benchmark aims to fill the current gap in the MARL community for assessing methodologies in more complex and realistic scenarios. My dissertation would focus on proposing and evaluating methods for scaling up multi-agent systems in two aspects: structural-wise increasing the number of reinforcement learning agents and temporal-wise extending the planning horizon and complexity of problem domains that agents are deployed in.
format	text
author	GENG, Minghong
author_facet	GENG, Minghong
author_sort	GENG, Minghong
title	Scaling up cooperative multi-agent reinforcement learning systems
title_short	Scaling up cooperative multi-agent reinforcement learning systems
title_full	Scaling up cooperative multi-agent reinforcement learning systems
title_fullStr	Scaling up cooperative multi-agent reinforcement learning systems
title_full_unstemmed	Scaling up cooperative multi-agent reinforcement learning systems
title_sort	scaling up cooperative multi-agent reinforcement learning systems
publisher	Institutional Knowledge at Singapore Management University
publishDate	2024
url	https://ink.library.smu.edu.sg/sis_research/9750 https://ink.library.smu.edu.sg/context/sis_research/article/10750/viewcontent/p2737__1_.pdf
_version_	1819113127453655040

Scaling up cooperative multi-agent reinforcement learning systems

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