  Recent works on ride-sharing order dispatching have highlighted the
importance of taking into account both the spatial and temporal dynamics in the
dispatching process for improving the transportation system efficiency. At the
same time, deep reinforcement learning has advanced to the point where it
achieves superhuman performance in a number of fields. In this work, we propose
a deep reinforcement learning based solution for order dispatching and we
conduct large scale online A/B tests on DiDi's ride-dispatching platform to
show that the proposed method achieves significant improvement on both total
driver income and user experience related metrics. In particular, we model the
ride dispatching problem as a Semi Markov Decision Process to account for the
temporal aspect of the dispatching actions. To improve the stability of the
value iteration with nonlinear function approximators like neural networks, we
propose Cerebellar Value Networks (CVNet) with a novel distributed state
representation layer. We further derive a regularized policy evaluation scheme
for CVNet that penalizes large Lipschitz constant of the value network for
additional robustness against adversarial perturbation and noises. Finally, we
adapt various transfer learning methods to CVNet for increased learning
adaptability and efficiency across multiple cities. We conduct extensive
offline simulations based on real dispatching data as well as online AB tests
through the DiDi's platform. Results show that CVNet consistently outperforms
other recently proposed dispatching methods. We finally show that the
performance can be further improved through the efficient use of transfer
learning.
