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Bayesian nonparametric learning for practical sequential decision making

Funding: 2021: $136,246
2022: $136,246
2020: $138,026

Project Member(s): Xuan, J.

Funding or Partner Organisation: Australian Research Council (ARC DECRA Scheme)
Australian Research Council (ARC DECRA Scheme)

Start year: 2020

Summary: 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.

FOR Codes: Decision Support and Group Support Systems, Pattern Recognition and Data Mining, Information Retrieval and Web Search, Information Processing Services (incl. Data Entry and Capture), Expanding Knowledge in the Information and Computing Sciences, Application Software Packages (excl. Computer Games), Modelling and simulation, Application software packages, Information systems, technologies and services not elsewhere classified