Dimension-reduced Reinforcement Learning for Large-scale Fleet Management

大规模车队管理的降维强化学习

• 批准号: DE200101310
• 负责人: Dr Wei Bian
• 金额: --
• 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: 2021
• 资助国家: 澳大利亚
• 起止时间: 2021-06-04 至 2027-06-03
• 项目状态: 未结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DE200101310
• 关键词: Dimension; reduced; Reinforcement; Learning; Large

This project aims to address the problems in large-scale fleet management to ensure the efficiency of tomorrow’s transportation models, such as on-demand ride-hailing and mobility-as-a-service. The expected outcomes of this project include improved techniques for optimising the utility of large fleets of vehicles, and particularly robust dimension-reduced reinforcement learning algorithms that are capable of handling the complex dynamics of supply and demand in transportation. The results should advance both research and technology in academia and the transportation industry and will also provide significant benefits to Australia and the international community by enhancing the energy-efficiency of and access to the mobility of the future.

该项目旨在解决大规模车队管理中的问题，以确保未来交通模式的效率，例如按需叫车和移动即服务。该项目的预期成果包括优化大型车队效用的改进技术，特别是能够处理运输中复杂的供需动态的鲁棒降维强化学习算法。研究结果将推动学术界和交通行业的研究和技术发展，并通过提高未来交通的能源效率和可及性，为澳大利亚和国际社会带来重大利益。