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Poster
Learn to Match with No Regret: Reinforcement Learning in Markov Matching Markets
Yifei Min · Tianhao Wang · Ruitu Xu · Zhaoran Wang · Michael Jordan · Zhuoran Yang

Wed Nov 30 02:00 PM -- 04:00 PM (PST) @ Hall J #1036
in Poster Session 4 »

We study a Markov matching market involving a planner and a set of strategic agents on the two sides of the market.At each step, the agents are presented with a dynamical context, where the contexts determine the utilities. The planner controls the transition of the contexts to maximize the cumulative social welfare, while the agents aim to find a myopic stable matching at each step. Such a setting captures a range of applications including ridesharing platforms. We formalize the problem by proposing a reinforcement learning framework that integrates optimistic value iteration with maximum weight matching. The proposed algorithm addresses the coupled challenges of sequential exploration, matching stability, and function approximation. We prove that the algorithm achieves sublinear regret.

Keywords: matching · sequential matching · optimal matching · dynamic matching

Author Information

Yifei Min (Yale University)
Tianhao Wang (Yale University)
Ruitu Xu (Yale University)
Zhaoran Wang (Northwestern University)
Michael Jordan (UC Berkeley)
Zhuoran Yang (Yale University)

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