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Grant Award View - GA64673-V2
Dimension-reduced Reinforcement Learning for Large-scale Fleet...
GA ID:
GA64673-V2
Agency:
Australian Research Council
Approval Date:
4-Dec-2019
Variation Publish Date:
18-May-2021
Variation Date:
4-Jun-2021
Category:
Science, Technology, Engineering and Mathematics (STEM) Research
Grant Term:
4-Jun-2021 to 3-Jun-2027
Value (AUD):
$426,918.00
(GST inclusive where applicable)
Varies:
GA64673
- Dimension-reduced Reinforcement Learning for Large-scale Fleet Management
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