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Grant Award View - GA64673

Dimension-reduced Reinforcement Learning for Large-scale Fleet...

Contact Details

ARC NCGP General Enquiries

:
02 6287 6600

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GA ID:
GA64673
Agency:
Australian Research Council
Approval Date:
4-Dec-2019
Publish Date:
5-Dec-2019
Category:
Science, Technology, Engineering and Mathematics (STEM) Research
Grant Term:
4-Jun-2021 to 3-Jun-2027
Original: 1-Jan-2020 to 31-Dec-2022
Value (AUD):
$0.00 (GST inclusive where applicable)
Original: $426,918.00
Variations:

One-off/Ad hoc:
No
Aggregate Grant Award:
No

PBS Program Name:
ARC 19/20 Discovery
Grant Program:
Discovery Early Career Researcher Award
Grant Activity:
Dimension-reduced Reinforcement Learning for Large-scale Fleet Management
Purpose:
This project aims to address the problems in large-scale fleet management to ensure the efficiency of tomorrow’s transportation models, such as on-demand ride-hailing and mobility-as-a-service. The expected outcomes of this project include improved techniques for optimising the utility of large fleets of vehicles, and particularly robust dimension-reduced reinforcement learning algorithms that are capable of handling the complex dynamics of supply and demand in transportation. The results should advance both research and technology in academia and the transportation industry and will also provide significant benefits to Australia and the international community by enhancing the energy-efficiency of and access to the mobility of the future.

GO ID:
GO Title:
Discovery Early Career Researcher Award for funding commencing in 2020
Internal Reference ID:
DE20 Round 1
Selection Process:
Targeted or Restricted Competitive

Confidentiality - Contract:
No
Confidentiality - Outputs:
No

Grant Recipient Details

Recipient Name:
University of Technology Sydney
Recipient ABN:
77 257 686 961

Grant Recipient Location

Suburb:
ULTIMO
Town/City:
ULTIMO
Postcode:
2007
State/Territory:
NSW
Country:
AUSTRALIA

Grant Delivery Location

State/Territory:
NSW
Postcode:
2007
Country:
AUSTRALIA

Contact Details

ARC NCGP General Enquiries

:
02 6287 6600

: