An End-to-End Reinforcement Learning Based Approach for Micro-View Order-Dispatching in Ride-Hailing

TL;DR

This paper introduces an end-to-end reinforcement learning approach for micro-view order dispatching in ride-hailing, modeling the problem with a two-layer MDP and a novel D2SN network to improve matching efficiency.

Contribution

It presents a unified, end-to-end RL framework with a new D2SN network for real-time order-driver assignment, outperforming traditional two-stage methods.

Findings

Significantly outperforms baselines in matching efficiency

Adapts to behavioral patterns for better performance

Demonstrates successful deployment in real-world scenarios

Abstract

Assigning orders to drivers under localized spatiotemporal context (micro-view order-dispatching) is a major task in Didi, as it influences ride-hailing service experience. Existing industrial solutions mainly follow a two-stage pattern that incorporate heuristic or learning-based algorithms with naive combinatorial methods, tackling the uncertainty of both sides' behaviors, including emerging timings, spatial relationships, and travel duration, etc. In this paper, we propose a one-stage end-to-end reinforcement learning based order-dispatching approach that solves behavior prediction and combinatorial optimization uniformly in a sequential decision-making manner. Specifically, we employ a two-layer Markov Decision Process framework to model this problem, and present \underline{D}eep \underline{D}ouble \underline{S}calable \underline{N}etwork (D2SN), an encoder-decoder structure network to generate order-driver assignments directly and stop assignments accordingly. Besides, by leveraging contextual dynamics, our approach can adapt to the behavioral patterns for better performance. Extensive experiments on Didi's real-world benchmarks justify that the proposed approach significantly outperforms competitive baselines in optimizing matching efficiency and user experience tasks. In addition, we evaluate the deployment outline and discuss the gains and experiences obtained during the deployment tests from the view of large-scale engineering implementation.