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Reinforcement Learning for Aviation Training
This project seeks to explore the use of a class of artificial intelligence algorithms called reinforcement learning for the purpose of aiding the training of new pilots. In the process, we seek to “teach” an algorithm how to fly an aircraft by exposing the AI pilot to a virtual environment and providing it with flight data and a goal. Alternatively, the algorithm could learn by observing human pilots. If this approach is successful, it could lead to better autopilot systems as well as teaching aids for new pilots by simulating their response to different flying environments and helping instructors select the most appropriate training exercises.
Faculty Supervisor:
Yoshua Bengio
Student:
Partner:
Paladin AI Inc
Discipline:
Computer science
Sector:
Professional, scientific and technical services
University:
Université de Montréal
Program:
Accelerate
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Mitacs is funded by the Government of Canada, the Government of Alberta, the Government of British Columbia, Research Manitoba, the Government of New Brunswick, the Government of Newfoundland and Labrador, the Government of Nova Scotia, the Government of Ontario, Innovation PEI, the Government of Quebec, the Government of Saskatchewan, and the Government of Yukon.