Bayesian nonparametric learning for practical sequential decision making

本项目旨在开发新的方法来支持不确定情况下的实际顺序决策。它期望为下一代序列决策铺平道路，该决策具有不确定性建模、高样本效率、有效的环境变化适应和自动奖励函数学习的独特特征。预期结果将通过新的数据建模深度学习模式推进机器学习知识，并通过新的深度强化学习方法推进顺序决策知识。这些发展在自动驾驶汽车、先进制造和动态定价方面具有直接的应用价值，为澳大利亚和世界带来科学、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.