A MULTIAGENT REINFORCEMENT LEARNING FRAMEWORK FOR OFF-POLICY EVALUATION IN TWO-SIDED MARKETS

• 成果类型: Article
• 署名作者: Shi, Chengchun; Wan, Runzhe; Song, Ge; Luo, Shikai; Zhu, Hongtu; Song, Rui
• 署名单位: University of London; London School Economics & Political Science; North Carolina State University; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/22-AOAS1700
• 发表日期: 2023
• 页码: 2701-2722
• 关键词: dynamic treatment regimes; Causal Inference; DECISION; performance

摘要

The two-sided markets, such as ride-sharing companies, often involve a group of subjects who are making sequential decisions across time and/or location. With the rapid development of smart phones and internet of things, they have substantially transformed the transportation landscape of human beings. In this paper we consider large-scale fleet management in ride-sharing companies that involve multiple units in different areas receiving sequences of products (or treatments) over time. Major technical challenges, such as policy evaluation, arise in those studies because: (i) spatial and temporal proximities induce interference between locations and times, and (ii) the large number of locations results in the curse of dimensionality. To address both challenges simultaneously, we introduce a multiagent reinforcement learning (MARL) framework for carrying policy evaluation in these studies. We propose novel estimators for mean outcomes under different products that are consistent despite the high dimensionality of state-action space. The proposed estimator works favorably in simulation experiments. We further illustrate our method using a real dataset obtained from a two-sided marketplace company to evaluate the effects of applying different subsidizing policies. A Python implementation of our proposed method is available in the Supplementary Material and also at https://github.com/RunzheStat/CausalMARL.