Player-optimal Stable Regret for Bandit Learning in Matching Markets

TL;DR

This paper introduces an algorithm for bandit learning in matching markets that achieves near-optimal stable regret bounds for players, significantly improving over previous methods especially when preference gaps are small.

Contribution

The paper proposes the explore-then-Gale-Shapley (ETGS) algorithm that bounds player-optimal stable regret by O(K log T / Δ^2), advancing the theoretical understanding of learning in matching markets.

Findings

Achieves polynomial regret bounds for player-optimal stable matching.

Improves upon previous results with exponential bounds under small preference gaps.

Matches lower bounds under certain preference conditions.

Abstract

The problem of matching markets has been studied for a long time in the literature due to its wide range of applications. Finding a stable matching is a common equilibrium objective in this problem. Since market participants are usually uncertain of their preferences, a rich line of recent works study the online setting where one-side participants (players) learn their unknown preferences from iterative interactions with the other side (arms). Most previous works in this line are only able to derive theoretical guarantees for player-pessimal stable regret, which is defined compared with the players' least-preferred stable matching. However, under the pessimal stable matching, players only obtain the least reward among all stable matchings. To maximize players' profits, player-optimal stable matching would be the most desirable. Though \citet{basu21beyond} successfully bring an upper bound for player-optimal stable regret, their result can be exponentially large if players' preference gap is small. Whether a polynomial guarantee for this regret exists is a significant but still open problem. In this work, we provide a new algorithm named explore-then-Gale-Shapley (ETGS) and show that the optimal stable regret of each player can be upper bounded by $O(K\log T/\Delta^2)$ where $K$ is the number of arms, $T$ is the horizon and $\Delta$ is the players' minimum preference gap among the first $N+1$-ranked arms. This result significantly improves previous works which either have a weaker player-pessimal stable matching objective or apply only to markets with special assumptions. When the preferences of participants satisfy some special conditions, our regret upper bound also matches the previously derived lower bound.