Bayesian nonparametric learning for practical sequential decision making

用于实际顺序决策的贝叶斯非参数学习

• 批准号: DE200100245
• 负责人: Dr Junyu Xuan
• 金额: $ 28.82万
• 依托单位: University of Technology Sydney
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Early Career Researcher Award
• 财政年份: 2020
• 资助国家: 澳大利亚
• 起止时间: 2020-05-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DE200100245
• 关键词: Bayesian; nonparametric; learning; practical; sequential

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.

该项目旨在开发新方法来支持不确定性情况下的实际顺序决策。它有望为下一代顺序决策铺平道路，其独特的特点是不确定性建模，高样本效率，有效的环境变化适应和自动奖励函数学习。预期成果将通过新的深度学习模式推进机器学习知识，用于数据建模和序列决策知识，以及新的深度强化学习方法。这些发展将立即应用于自动驾驶汽车、先进制造和动态定价，为澳大利亚和世界带来科学、经济和社会效益。