Full Citation

Title: A Multiagent Reinforcement Learning Framework Foroff-policy Evaluation in Two-sided Markets

Citation Type: Journal Article

Publication Year: 2023

ISBN:

ISSN: 1941-7330

DOI:

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PMID:

Abstract: 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 smartphones 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 out 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.

Url: https://imstat.org/publications/aoas/aoas_17_4/aoas_17_4.pdf

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Authors: Shi, Chengchun; Wan, Runzhe; Song, Ge; Luo, Shikai; Zhu, Hongtu; Song, Rui

Periodical (Full): The Annals of Applied Statistics

Issue: 4

Volume: 17

Pages: 2701-2722

Data Collections: IPUMS NHGIS

Topics: Labor Force and Occupational Structure, Work, Family, and Time

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