Reinforcement Learning in Differential Games: A Learning Invader for the Guarding a Territory Game
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This thesis explores the use of a learning algorithm in the "guarding a territory" game. The game occurs in continuous time, where a learning invader tries to get as close as possible to a territory before being captured by a guard. Previous research has let only the guard learn. We will examine the other possibility of the game, in which only the invader is learning. Furthermore, the guard is superior to the invader. We will also consider using models with non-holonomic constraints. A control system is designed and optimized for the invader to play the game. The thesis shows how the learning system is able to adapt to an unknown environment. We evaluated our system's performance through different simulations. We conducted experiments at the Royal Military College of Canada using real robots in a real-life environment. Our results show that our learning invader behaved rationally in different circumstances.
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Copyright © 2015 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. No part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.
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raslan-reinforcementlearningindifferentialgamesa.pdf | 2023-05-04 | Public | Download |