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Efficient Ridesharing Order Dispatching with Mean Field Multi-Agent Reinforcement Learning.

, , , , , , , and . WWW, page 983-994. ACM, (2019)

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An alternating direction method for total variation denoising., , and . Optim. Methods Softw., 30 (3): 594-615 (2015)Structured Sparsity via Alternating Direction Methods., and . J. Mach. Learn. Res., (2012)KDD 2022 Workshop on Decision Intelligence and Analytics for Online Marketplaces: Jobs, Ridesharing, Retail, and Beyond., , , , , , and . SIGKDD Explor., 24 (2): 78-80 (2022)Deep Reinforcement Learning for Multi-driver Vehicle Dispatching and Repositioning Problem., , , , , , , , and . ICDM, page 1090-1095. IEEE, (2019)A Unified Representation Framework for Rideshare Marketplace Equilibrium and Efficiency., and . SIGSPATIAL/GIS, page 38:1-38:11. ACM, (2023)Sim2Rec: A Simulator-based Decision-making Approach to Optimize Real-World Long-term User Engagement in Sequential Recommender Systems., , , , , , , and . ICDE, page 3389-3402. IEEE, (2023)Optimizing Bike-Share Repositioning: Networked Inventory Management with Spatiotemporal Modeling., , , , and . IEEE BigData, page 1438-1448. IEEE, (2021)A Deep Value-network Based Approach for Multi-Driver Order Dispatching., , , , , , , and . KDD, page 1780-1790. ACM, (2019)Low-Rank Tensor Recovery for Geo-Demand Estimation in Online Retailing., , and . INNS Conference on Big Data, volume 53 of Procedia Computer Science, page 239-247. Elsevier, (2015)Value Function is All You Need: A Unified Learning Framework for Ride Hailing Platforms., , , , , , , , and . KDD, page 3605-3615. ACM, (2021)