Online Learning Bipartite Matching with Non-stationary Distributions

Author:

Chen Weirong1ORCID,Zheng Jiaqi1,Yu Haoyu1,Chen Guihai1,Chen Yixin2,Li Dongsheng2

Affiliation:

1. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China

2. National Lab for Parallel and Distributed Processing, National University of Defense Technology, Changsha, China

Abstract

Online bipartite matching has attracted wide interest since it can successfully model the popular online car-hailing problem and sharing economy. Existing works consider this problem under either adversary setting or i.i.d. setting. The former is too pessimistic to improve the performance in the general case; the latter is too optimistic to deal with the varying distribution of vertices. In this article, we initiate the study of the non-stationary online bipartite matching problem, which allows the distribution of vertices to vary with time and is more practical. We divide the non-stationary online bipartite matching problem into two subproblems, the matching problem and the selecting problem, and solve them individually. Combining Batch algorithms and deep Q-learning networks, we first construct a candidate algorithm set to solve the matching problem. For the selecting problem, we use a classical online learning algorithm, Exp3, as a selector algorithm and derive a theoretical bound. We further propose CDUCB as a selector algorithm by integrating distribution change detection into UCB. Rigorous theoretical analysis demonstrates that the performance of our proposed algorithms is no worse than that of any candidate algorithms in terms of competitive ratio. Finally, extensive experiments show that our proposed algorithms have much higher performance for the non-stationary online bipartite matching problem comparing to the state-of-the-art.

Funder

National Key Research and Development Program of China

China NSF

China NSF of Jiangsu Province

Open Fund of PDL

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science

Reference39 articles.

1. Edge Weighted Online Windowed Matching

2. The Nonstochastic Multiarmed Bandit Problem

3. Adaptively tracking the best bandit arm with an unknown number of distribution changes;Ortner R.;Proceedings of the 32nd Conference on Learning Theory,2019

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