Deep reinforcement learning with applications in transportation

Zhiwei Qin, Jian Tang, Jieping Ye

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This tutorial aims to provide the audience with a guided introduction to deep reinforcement learning (DRL) with specially curated application case studies in transportation. The tutorial covers both theory and practice, with more emphasis on the practical aspects of DRL that are pertinent to tackle transportation challenges. Some core examples include online ride order dispatching, fleet management, traffic signals control, route planning, and autonomous driving.

Original languageEnglish (US)
Title of host publicationKDD 2019 - Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Pages3201-3202
Number of pages2
ISBN (Electronic)9781450362016
DOIs
StatePublished - Jul 25 2019
Event25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2019 - Anchorage, United States
Duration: Aug 4 2019Aug 8 2019

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Conference

Conference25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2019
CountryUnited States
CityAnchorage
Period8/4/198/8/19

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Keywords

  • Deep reinforcement learning
  • Intelligent transportation
  • Ridesharing

ASJC Scopus subject areas

  • Software
  • Information Systems

Cite this

Qin, Z., Tang, J., & Ye, J. (2019). Deep reinforcement learning with applications in transportation. In KDD 2019 - Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 3201-3202). (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). Association for Computing Machinery. https://doi.org/10.1145/3292500.3332299