Deep reinforcement learning with applications in transportation

Zhiwei Qin; Jian Tang; Jieping Ye

doi:10.1145/3292500.3332299

Deep reinforcement learning with applications in transportation

Zhiwei Qin, Jian Tang, Jieping Ye

Department of Electrical Engineering & Computer Science

Research output: Chapter in Book/Entry/Poem › Conference contribution

10 Scopus citations

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 language	English (US)
Title of host publication	KDD 2019 - Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Publisher	Association for Computing Machinery
Pages	3201-3202
Number of pages	2
ISBN (Electronic)	9781450362016
DOIs	https://doi.org/10.1145/3292500.3332299
State	Published - Jul 25 2019
Event	25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2019 - Anchorage, United States Duration: Aug 4 2019 → Aug 8 2019

Publication series

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

Conference

Conference	25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2019
Country/Territory	United States
City	Anchorage
Period	8/4/19 → 8/8/19

Keywords

Deep reinforcement learning
Intelligent transportation
Ridesharing

ASJC Scopus subject areas

Software
Information Systems

Access to Document

10.1145/3292500.3332299

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

Deep reinforcement learning with applications in transportation. / Qin, Zhiwei; Tang, Jian; Ye, Jieping.
KDD 2019 - Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, 2019. p. 3201-3202 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

Research output: Chapter in Book/Entry/Poem › Conference contribution

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. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery, pp. 3201-3202, 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2019, Anchorage, United States, 8/4/19. https://doi.org/10.1145/3292500.3332299

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

Qin, Zhiwei ; Tang, Jian ; Ye, Jieping. / Deep reinforcement learning with applications in transportation. KDD 2019 - Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, 2019. pp. 3201-3202 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

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Deep reinforcement learning with applications in transportation

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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