A reinforcement learning based bipartite matching system

Reinforcement learning is an area of machine learning that pertains to how intelligent agents should respond to the constantly changing aspects of the environment with the objective to maximize the notion of cumulative reward. Reinforcement learning has been a widely used tool in various disciplines...

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Main Author: Khairul Amiru Ahmad Mohr
Other Authors: Cheng Long
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2021
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Online Access:https://hdl.handle.net/10356/148067
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Institution: Nanyang Technological University
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spelling sg-ntu-dr.10356-1480672021-04-22T12:06:47Z A reinforcement learning based bipartite matching system Khairul Amiru Ahmad Mohr Cheng Long School of Computer Science and Engineering c.long@ntu.edu.sg Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Reinforcement learning is an area of machine learning that pertains to how intelligent agents should respond to the constantly changing aspects of the environment with the objective to maximize the notion of cumulative reward. Reinforcement learning has been a widely used tool in various disciplines such as resource management, multi-agent systems, games, etc. The scope of this project aims to utilize RL to tackle the problem of bipartite matching, which is a form of matching where the set of edges are chosen such that no two edges share the same endpoint. Many real-world problems can be modeled as bipartite matching very naturally. For instance, consider a subset of applicants and a subset of job vacancies. Each job vacancy can only accept one applicant and one applicant can only be appointed one job. The relations between the subset of applicants and the subset of job vacancies accurately describe a bipartite matching problem when we try to maximize the number of matches that can be made within those subsets. If we extend this perspective towards other use-cases, tackling the problem of bipartite matching becomes largely relevant. This FYP is based on the study done by a Ph.D. student under the same supervisor on his algorithm: Adaptive Holding for Online Bottleneck Matching with Delays. It is geared towards developing a tool that aids visualization of the algorithm for better comprehension and grasp of the inner workings of the algorithm. Bachelor of Engineering (Computer Science) 2021-04-22T12:06:47Z 2021-04-22T12:06:47Z 2021 Final Year Project (FYP) Khairul Amiru Ahmad Mohr (2021). A reinforcement learning based bipartite matching system. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/148067 https://hdl.handle.net/10356/148067 en SCSE20-0421 application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
spellingShingle Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Khairul Amiru Ahmad Mohr
A reinforcement learning based bipartite matching system
description Reinforcement learning is an area of machine learning that pertains to how intelligent agents should respond to the constantly changing aspects of the environment with the objective to maximize the notion of cumulative reward. Reinforcement learning has been a widely used tool in various disciplines such as resource management, multi-agent systems, games, etc. The scope of this project aims to utilize RL to tackle the problem of bipartite matching, which is a form of matching where the set of edges are chosen such that no two edges share the same endpoint. Many real-world problems can be modeled as bipartite matching very naturally. For instance, consider a subset of applicants and a subset of job vacancies. Each job vacancy can only accept one applicant and one applicant can only be appointed one job. The relations between the subset of applicants and the subset of job vacancies accurately describe a bipartite matching problem when we try to maximize the number of matches that can be made within those subsets. If we extend this perspective towards other use-cases, tackling the problem of bipartite matching becomes largely relevant. This FYP is based on the study done by a Ph.D. student under the same supervisor on his algorithm: Adaptive Holding for Online Bottleneck Matching with Delays. It is geared towards developing a tool that aids visualization of the algorithm for better comprehension and grasp of the inner workings of the algorithm.
author2 Cheng Long
author_facet Cheng Long
Khairul Amiru Ahmad Mohr
format Final Year Project
author Khairul Amiru Ahmad Mohr
author_sort Khairul Amiru Ahmad Mohr
title A reinforcement learning based bipartite matching system
title_short A reinforcement learning based bipartite matching system
title_full A reinforcement learning based bipartite matching system
title_fullStr A reinforcement learning based bipartite matching system
title_full_unstemmed A reinforcement learning based bipartite matching system
title_sort reinforcement learning based bipartite matching system
publisher Nanyang Technological University
publishDate 2021
url https://hdl.handle.net/10356/148067
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