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

Pattern Transfer Learning for Reinforcement Learning in Order Dispatching