  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.

Machine Learning;Artificial Intelligence