  Autonomous Mobility-on-Demand (AMoD) systems are an evolving mode of
transportation in which a centrally coordinated fleet of self-driving vehicles
dynamically serves travel requests. The control of these systems is typically
formulated as a large network optimization problem, and reinforcement learning
(RL) has recently emerged as a promising approach to solve the open challenges
in this space. Recent centralized RL approaches focus on learning from online
data, ignoring the per-sample-cost of interactions within real-world
transportation systems. To address these limitations, we propose to formalize
the control of AMoD systems through the lens of offline reinforcement learning
and learn effective control strategies using solely offline data, which is
readily available to current mobility operators. We further investigate design
decisions and provide empirical evidence based on data from real-world mobility
systems showing how offline learning allows to recover AMoD control policies
that (i) exhibit performance on par with online methods, (ii) allow for
sample-efficient online fine-tuning and (iii) eliminate the need for complex
simulation environments. Crucially, this paper demonstrates that offline RL is
a promising paradigm for the application of RL-based solutions within
economically-critical systems, such as mobility systems.
