Abstract
Inefficient supply-demand matching makes the fleet management a research hotpot in ride-sharing platforms. With the booming of mobile network services, it is promising to abate the supply-demand gap with effective vehicle dispatching. In this article, we propose a QRewriter - Dueling Deep Q-Network (QRewriter-DDQN) algorithm, to dispatch multiple available vehicles in ahead to the locations with high demand to serve more orders. The QRewriter-DDQN algorithm factorizes into a Dueling Deep Q-Network (DDQN) module and a QRewriter module, which are parameterized by neural networks and Q-table with Reinforcement Learning (RL) methods, respectively. Particularly, DDQN module utilizes the Kullback-Leibler (KL) distribution distance between supply (available vehicles) and demand (orders) as excitation to capture the complex dynamic variations of supply-demand. Afterwards, the QRewriter module learns to improve the DDQN dispatching policy with the streamlined and effective Q-table in RL. Importantly, the higher performance improvement space of the DDQN dispatching policy can be obtained by aggregating QRewriter state into low-dimension meta state. A simulator is designed to train and test the performance of QRewriter-DDQN, the experiment results show the significant improvement of QRewriter-DDQN in terms of order response rate.
Highlights
With the vigorous development of ride-sharing platforms, such as DiDi Chuxing [1] and Uber [2], transportation becomes convenient and flexible
The fleet management is a complex dynamic process as the dispatching decisions for current vehicles will affect the gap of future traffic supply-demand
We propose a multi-vehicle dispatching algorithm, QRewriter - Dueling Deep Q-Network (QRewriter-DDQN) algorithm, to dispatch available vehicles to high-demand locations to serve more orders
Summary
With the vigorous development of ride-sharing platforms, such as DiDi Chuxing [1] and Uber [2], transportation becomes convenient and flexible. The authors in [8] designed a layered multi-agent DRL algorithm to solve the joint order dispatching and vehicle dispatching problem with inputting the real-time vehicle distribution and order distribution. In [11], a distributed DRL algorithm with supply-demand distribution as input was proposed to design vehicle routing to improve the performance of ride-sharing platform. A model-free DRL algorithm was proposed in [12] to reduce unserved orders by solving the dynamic fleet management problem, and the complex supply-demand distribution as a part of state input. The authors in [14] proposed a multi-agent DRL algorithm to dispatch available vehicles to high-demand locations. We propose a multi-vehicle dispatching algorithm, QRewriter - Dueling Deep Q-Network (QRewriter-DDQN) algorithm, to dispatch available vehicles to high-demand locations to serve more orders.
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