Bayesian nonparametric learning for practical sequential decision making

该项目旨在开发新的方法来支持不确定情况下的实际序贯决策。它有望为下一代序贯决策铺平道路，其独特的特征是不确定性建模、高样本效率、高效的环境变化适应和自动奖励函数学习。预期结果将通过一种用于数据建模的新的深度学习模式来推进机器学习知识，并通过一种新的深度强化学习方法来促进顺序决策知识。这些发展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了科学、经济和社会效益。

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.