Multi-Agent Reinforcement Learning for Autonoumous Vehicles

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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.