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.
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