Summary of An End-to-end Reinforcement Learning Based Approach For Micro-view Order-dispatching in Ride-hailing, by Xinlang Yue et al.
An End-to-End Reinforcement Learning Based Approach for Micro-View Order-Dispatching in Ride-Hailing
by Xinlang Yue, Yiran Liu, Fangzhou Shi, Sihong Luo, Chen Zhong, Min Lu, Zhe Xu
First submitted to arxiv on: 20 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a one-stage end-to-end reinforcement learning-based order-dispatching approach for ride-hailing services like Didi. The existing industrial solutions typically follow a two-stage pattern, combining heuristic or learning-based algorithms with naive combinatorial methods to tackle uncertainty in emerging timings, spatial relationships, and travel duration. This novel approach employs a two-layer Markov Decision Process framework to model the problem and introduces the Deep Double Scalable Network (D2SN) for direct order-driver assignment generation and stopping assignments accordingly. The proposed method can adapt to behavioral patterns through contextual dynamics, outperforming competitive baselines in optimizing matching efficiency and user experience tasks. Extensive experiments on real-world benchmarks validate the approach’s effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making better decisions when assigning drivers to pick up passengers. Right now, ride-hailing companies like Didi use a two-step process that combines simple rules with machine learning algorithms to decide who picks up whom. The problem is that this doesn’t take into account things like how long it will take for the driver to get to the passenger or what’s happening in real-time. To fix this, the researchers propose a new way of doing things using reinforcement learning and a special kind of neural network called D2SN. This approach can adapt to different situations and make better decisions than current methods. |
Keywords
» Artificial intelligence » Machine learning » Neural network » Reinforcement learning
