Adaptive Dynamic Bipartite Graph Matching: A Reinforcement Learning Approach

Online bipartite graph matching is attracting growing research attention due to the development of dynamic task assignment in sharing economy applications, where tasks need be assigned dynamically to workers. Past studies lack practicability in terms of both problem formulation and solution framewor...

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Veröffentlicht in:Data engineering S. 1478 - 1489
Hauptverfasser: Wang, Yansheng, Tong, Yongxin, Long, Cheng, Xu, Pan, Xu, Ke, Lv, Weifeng
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.04.2019
Schlagworte:
Adaptation models
Bipartite graph
bipartite graph matching
online algorithm
Reinforcement learning
Resource management
Sharing economy
Stochastic processes
Task analysis
ISSN:2375-026X
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Zusammenfassung:Online bipartite graph matching is attracting growing research attention due to the development of dynamic task assignment in sharing economy applications, where tasks need be assigned dynamically to workers. Past studies lack practicability in terms of both problem formulation and solution framework. On the one hand, some problem settings in prior online bipartite graph matching research are impractical for real-world applications. On the other hand, existing solutions to online bipartite graph matching are inefficient due to the unnecessary real-time decision making. In this paper, we propose the dynamic bipartite graph matching (DBGM) problem to be better aligned with real-world applications and devise a novel adaptive batch-based solution framework with a constant competitive ratio. As an effective and efficient implementation of the solution framework, we design a reinforcement learning based algorithm, called Restricted Q-learning (RQL), which makes near-optimal decisions on batch splitting. Extensive experimental results on both real and synthetic datasets show that our methods outperform the state-of-the-arts in terms of both effectiveness and efficiency.
ISSN:2375-026X
DOI:10.1109/ICDE.2019.00133