  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 multi-agent 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 at
https://github.com/RunzheStat/CausalMARL.
