Can Sophisticated Dispatching Strategy Acquired by Reinforcement
  Learning? - A Case Study in Dynamic Courier Dispatching System
  In this paper, we study a courier dispatching problem (CDP) raised from an
online pickup-service platform of Alibaba. The CDP aims to assign a set of
couriers to serve pickup requests with stochastic spatial and temporal arrival
rate among urban regions. The objective is to maximize the revenue of served
requests given a limited number of couriers over a period of time. Many online
algorithms such as dynamic matching and vehicle routing strategy from existing
literature could be applied to tackle this problem. However, these methods rely
on appropriately predefined optimization objectives at each decision point,
which is hard in dynamic situations. This paper formulates the CDP as a Markov
decision process (MDP) and proposes a data-driven approach to derive the
optimal dispatching rule-set under different scenarios. Our method stacks
multi-layer images of the spatial-and-temporal map and apply multi-agent
reinforcement learning (MARL) techniques to evolve dispatching models. This
method solves the learning inefficiency caused by traditional centralized MDP
modeling. Through comprehensive experiments on both artificial dataset and
real-world dataset, we show: 1) By utilizing historical data and considering
long-term revenue gains, MARL achieves better performance than myopic online
algorithms; 2) MARL is able to construct the mapping between complex scenarios
to sophisticated decisions such as the dispatching rule. 3) MARL has the
scalability to adopt in large-scale real-world scenarios.
