Multi-Agent Reinforcement Learning for Autonoumous Vehicles

自动驾驶汽车的多智能体强化学习

• 批准号: RGPIN-2017-06379
• 负责人: Schwartz, Howard
• 金额: $ 1.82万
• 依托单位: Carleton University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=638970
• 关键词: Multi; Agent; Reinforcement; Learning; Autonoumous

The long term objective of this research is to create a system of machines and devices that can effectively learn how to work together in a changing environment. We are proposing such systems for the multi-robot application. The idea is to have many unmanned vehicles and sensors working together and learning how to adapt to their environment. Applications include the security of industrial facilities and border regions and for teams of autonomous vehicles that can secure territory without endangering human life. In these cases combinations of vision systems and various types of unmanned vehicles will learn how to work together to secure the region and address any dangers. The interaction of artificial intelligence with actions of multiple vehicles and devices will be a huge leap forward in the development of artificial intelligence. This work is specifically important for those in the security and defence industries, the robotics industry and for those working on the development of unmanned aerial vehicles (drones) and self-driving cars. The industrial impact of this work will be far reaching.

这项研究的长期目标是创建一个机器和设备系统，可以有效地学习如何在不断变化的环境中协同工作。我们提出这样的系统的多机器人应用。这个想法是让许多无人驾驶车辆和传感器一起工作，并学习如何适应它们的环境。应用包括工业设施和边境地区的安全，以及可以在不危及人类生命的情况下保护领土的自动驾驶车辆团队。在这些情况下，视觉系统和各种类型的无人驾驶车辆的组合将学习如何共同努力，以确保该地区的安全并解决任何危险。人工智能与多个车辆和设备的动作交互将是人工智能发展的巨大飞跃。 这项工作对于安全和国防行业、机器人行业以及从事无人驾驶飞行器（无人机）和自动驾驶汽车开发的人员特别重要。这项工作的工业影响将是深远的。