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arXiv · 2109.10380

Deep Policies for Online Bipartite Matching: A Reinforcement Learning Approach

Mohammad Ali Alomrani·Reza Moravej·Elias B. Khalil
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Abstract

The challenge in the widely applicable online matching problem lies in making irrevocable assignments while there is uncertainty about future inputs. Most theoretically-grounded policies are myopic or greedy in nature. In real-world applications where the matching process is repeated on a regular basis, the underlying data distribution can be leveraged for better decision-making. We present an end-to-end Reinforcement Learning framework for deriving better matching policies based on trial-and-error on historical data. We devise a set of neural network architectures, design feature representations, and empirically evaluate them across two online matching problems: Edge-Weighted Online Bipartite Matching and Online Submodular Bipartite Matching. We show that most of the learning approaches perform consistently better than classical baseline algorithms on four synthetic and real-world datasets. On average, our proposed models improve the matching quality by 3--10\% on a variety of synthetic and real-world datasets. Our code is publicly available at https://github.com/lyeskhalil/CORL.

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Mohammad Ali Alomrani, Reza Moravej, Elias B. Khalil. 2021-09-21. Deep Policies for Online Bipartite Matching: A Reinforcement Learning Approach. https://arxiv.org/abs/2109.10380

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