This thesis explores the use of a learning algorithm in the "guarding a territory" game. The game occurs in continuous time, where a single learning invader tries to get as close as possible to a territory before being captured by a guard. Previous research has approached the problem by letting only the guard learn. We will examine the other possibility of the game, in which only the invader is learning. Furthermore, in our case the guard is superior (faster) 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 and reach the Nash Equilibrium. The thesis shows how the learning system is able to adapt to an unknown environment. We evaluated our system's performance through different simulations and compared it to the Nash Equilibrium.We conducted experiments at the Royal Military College of Canada using real robots.Our experiments verified our simulation results in a real-life environment. Our results show that our learning invader behaved rationally in different circumstances.iv

This paper explores the use of a learning algorithm in the "guarding a territory" game. The game occurs in continuous time, where a single learning invader tries to get as close as possible to a territory before being captured by a guard. Previous research has approached the problem by letting only the guard learn. We will examine the other possibility of the game, in which only the invader is going to learn. Furthermore, in our case 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 and reach Nash-Equilibrium. The paper finally shows how the learning system is able to adapt itself. The system's performance is evaluated through different simulations and compared to the Nash-Equilibrium. Experiments with real robots were conducted and verified our simulations in a real-life environment. Our results show that our learning invader behaved rationally in different circumstances.

This paper explores the use of a learning algorithm in the "guarding a territory" game. The game occurs in continuous time, where a single learning invader tries to get as close as possible to a territory before being captured by a guard. Previous research has approached the problem by letting only the guard learn. We will examine the other possibility of the game, in which only the invader is going to learn. Furthermore, in our case the guard is superior (faster) 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 and reach Nash Equilibrium. The paper shows how the learning system is able to adapt itself. The system's performance is evaluated through different simulations and compared to the Nash Equilibrium. Experiments with real robots were conducted and verified our simulations in a real-life environment. Our results show that our learning invader behaved rationally in different circumstances.