  The future of mobility-as-a-Service (Maas)should embrace an integrated system
of ride-hailing, street-hailing and ride-sharing with optimised intelligent
vehicle routing in response to a real-time, stochastic demand pattern. We aim
to optimise routing policies for a large fleet of vehicles for street-hailing
services, given a stochastic demand pattern in small to medium-sized road
networks. A model-based dispatch algorithm, a high performance model-free
reinforcement learning based algorithm and a novel hybrid algorithm combining
the benefits of both the top-down approach and the model-free reinforcement
learning have been proposed to route the \emph{vacant} vehicles. We design our
reinforcement learning based routing algorithm using proximal policy
optimisation and combined intrinsic and extrinsic rewards to strike a balance
between exploration and exploitation. Using a large-scale agent-based
microscopic simulation platform to evaluate our proposed algorithms, our
model-free reinforcement learning and hybrid algorithm show excellent
performance on both artificial road network and community-based Singapore road
network with empirical demands, and our hybrid algorithm can significantly
accelerate the model-free learner in the process of learning.
