  This paper proposes a reinforcement learning approach for nightly offline
rebalancing operations in free-floating electric vehicle sharing systems
(FFEVSS). Due to sparse demand in a network, FFEVSS require relocation of
electrical vehicles (EVs) to charging stations and demander nodes, which is
typically done by a group of drivers. A shuttle is used to pick up and drop off
drivers throughout the network. The objective of this study is to solve the
shuttle routing problem to finish the rebalancing work in the minimal time. We
consider a reinforcement learning framework for the problem, in which a central
controller determines the routing policies of a fleet of multiple shuttles. We
deploy a policy gradient method for training recurrent neural networks and
compare the obtained policy results with heuristic solutions. Our numerical
studies show that unlike the existing solutions in the literature, the proposed
methods allow to solve the general version of the problem with no restrictions
on the urban EV network structure and charging requirements of EVs. Moreover,
the learned policies offer a wide range of flexibility resulting in a
significant reduction in the time needed to rebalance the network.
