  Bike-Sharing Systems provide eco-friendly urban mobility, contributing to the
alleviation of traffic congestion and to healthier lifestyles. Efficiently
operating such systems and maintaining high customer satisfaction is
challenging due to the stochastic nature of trip demand, leading to full or
empty stations. Devising effective rebalancing strategies using vehicles to
redistribute bikes among stations is therefore of uttermost importance for
operators. As a promising alternative to classical mathematical optimization,
reinforcement learning is gaining ground to solve sequential decision-making
problems. This paper introduces a spatio-temporal reinforcement learning
algorithm for the dynamic rebalancing problem with multiple vehicles. We first
formulate the problem as a Multi-agent Markov Decision Process in a continuous
time framework. This allows for independent and cooperative vehicle
rebalancing, eliminating the impractical restriction of time-discretized models
where vehicle departures are synchronized. A comprehensive simulator under the
first-arrive-first-serve rule is then developed to facilitate the learning
process by computing immediate rewards under diverse demand scenarios. To
estimate the value function and learn the rebalancing policy, various Deep
Q-Network configurations are tested, minimizing the lost demand. Experiments
are carried out on various datasets generated from historical data, affected by
both temporal and weather factors. The proposed algorithms outperform
benchmarks, including a multi-period Mixed-Integer Programming model, in terms
of lost demand. Once trained, it yields immediate decisions, making it suitable
for real-time applications. Our work offers practical insights for operators
and enriches the integration of reinforcement learning into dynamic rebalancing
problems, paving the way for more intelligent and robust urban mobility
solutions.
