  Order dispatch is one of the central problems to ride-sharing platforms.
Recently, value-based reinforcement learning algorithms have shown promising
performance on this problem. However, in real-world applications, the
non-stationarity of the demand-supply system poses challenges to re-utilizing
data generated in different time periods to learn the value function. In this
work, motivated by the fact that the relative relationship between the values
of some states is largely stable across various environments, we propose a
pattern transfer learning framework for value-based reinforcement learning in
the order dispatch problem. Our method efficiently captures the value patterns
by incorporating a concordance penalty. The superior performance of the
proposed method is supported by experiments.
