Real-Time Rideshare Driver Supply Values Using Online Reinforcement Learning-Reference-Cited by-同舟云学术

Real-Time Rideshare Driver Supply Values Using Online Reinforcement Learning

Published:2022-08-14 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
language:
Short-container-title:

Author:

Han Benjamin¹,Lee Hyungjun²,Martin Sébastien³

Affiliation:

1. OpenSea, San Francisco, CA, USA

2. Snap Inc., San Francisco, CA, USA

3. Northwestern University, Evanston, IL, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3534678.3539141

Reference16 articles.

1. Surge Pricing Solves the Wild Goose Chase

2. Giuseppe DeCandia Deniz Hastorun Madan Jampani Gunavardhan Kakulapati Avinash Lakshman Alex Pilchin Swaminathan Sivasubramanian Peter Vosshall and Werner Vogels. 2007. Dynamo: amazon's highly available key-value store.. In SOSP. ACM 205--220. Giuseppe DeCandia Deniz Hastorun Madan Jampani Gunavardhan Kakulapati Avinash Lakshman Alex Pilchin Swaminathan Sivasubramanian Peter Vosshall and Werner Vogels. 2007. Dynamo: amazon's highly available key-value store.. In SOSP. ACM 205--220.

3. Benjamin Han. 2020. Open Source Implementation using TD(0). https://www.biendata.xyz/forum/view_post_category/1048/ Benjamin Han. 2020. Open Source Implementation using TD(0). https://www.biendata.xyz/forum/view_post_category/1048/

4. Budget Allocation as a Multi-Agent System of Contextual & Continuous Bandits

5. Geoffrey Hinton. 2014. rmsprop: Divide the gradient by a running average of its recent magnitude. https://www.cs.toronto.edu/ tijmen/csc321/slides/lecture_slides_lec6.pdf Geoffrey Hinton. 2014. rmsprop: Divide the gradient by a running average of its recent magnitude. https://www.cs.toronto.edu/ tijmen/csc321/slides/lecture_slides_lec6.pdf

Cited by 5 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Optimizing Long-Term Efficiency and Fairness in Ride-Hailing Under Budget Constraint via Joint Order Dispatching and Driver Repositioning;IEEE Transactions on Knowledge and Data Engineering;2024-07

2. Learning to Control Autonomous Fleets from Observation via Offline Reinforcement Learning;2024 European Control Conference (ECC);2024-06-25

3. A Better Match for Drivers and Riders: Reinforcement Learning at Lyft;INFORMS Journal on Applied Analytics;2024-01

4. A Unified Representation Framework for Rideshare Marketplace Equilibrium and Efficiency;Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems;2023-11-13

5. Towards Supply-Demand Equilibrium with Ridesharing: An Elastic Order Dispatching Algorithm in MoD System;IEEE Transactions on Mobile Computing;2023