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Grant Award View - GA64796-V3
Bayesian nonparametric learning for practical sequential decision making
GA ID:
GA64796-V3
Agency:
Australian Research Council
Approval Date:
4-Dec-2019
Variation Publish Date:
14-Oct-2021
Variation Date:
12-Oct-2021
Category:
Science, Technology, Engineering and Mathematics (STEM) Research
Grant Term:
1-May-2020 to 30-Apr-2023
Value (AUD):
$410,518.00
(GST inclusive where applicable)
Varies:
GA64796
- Bayesian nonparametric learning for practical sequential decision making
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:
Bayesian nonparametric learning for practical sequential decision making
Purpose:
This project aims to develop new methods to support practical sequential decision making under uncertainty. It expects to pave the way for the next generation of sequential decision making uniquely characterised by uncertainty modelling, high sample-efficiency, efficient environment change adaptation, and automatical reward function learning. The expected outcomes will advance machine learning knowledge with a new deep learning schema for data modelling and sequential decision-making knowledge with a novel deep reinforcement learning methodology. These developments have immediate applications in autonomous vehicles, advanced manufacturing, and dynamic pricing, with scientific, economic, and social benefits for Australia and the world.
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