多人竞争下，用线性上下文强化学习实现稳定匹配。
Competing Bandits in Decentralized Contextual Matching Markets
- 基于线性上下文建模玩家偏好，动态识别环境变化。
- 实现对数级后悔，与臂数量无关，适合大规模市场。
- 适用于资源受限的分布式匹配场景，如招聘、约会平台。
近年来，多智能体资源受限的序列学习匹配市场受到广泛关注。本文研究双边匹配市场中的去中心化学习问题，其中需求方（即玩家或智能体）争夺供给方（即臂），且偏好随时间变化。受线性上下文老虎机框架启发，我们假设每个玩家的臂均值可由已知特征向量和未知（玩家特异）参数的线性函数表示。此外，每轮的偏好依赖于一个潜在环境，该环境在不同轮次间非平稳变化。我们提出学习算法，同时识别潜在环境并获得稳定匹配。所提算法实现实例相关对数级后悔，且其性能独立于臂的数量，因此适用于大规模市场。
原文摘要 · Abstract (English)
Sequential learning in a multi-agent resource constrained matching market has received significant interest in the past few years. We study decentralized learning in two-sided matching markets where the demand side (aka players or agents) competes for the supply side (aka arms) with potentially time-varying preferences to obtain a stable match. Motivated by the linear contextual bandit framework, we assume that for each agent, an arm-mean may be represented by a linear function of a known feature vector and an unknown (agent-specific) parameter. Moreover, the preferences over arms depend on a latent environment in each round, where the latent environment varies across rounds in a non-stationary manner. We propose learning algorithms to identify the latent environment and obtain stable matchings simultaneously. Our proposed algorithms achieve instance-dependent logarithmic regret, scaling independently of the number of arms, and hence applicable for a large market.
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