Deception In The Game of Guarding Multiple Territories: A Machine Learning Approach
Amirhossein Asgharnia, Howard M. Schwartz, Mohamed Maher Atia · 2020
In this paper, a deceptive version of guarding a territory in a grid world is proposed. Like the original version, a defender tries to intercept an invader before it invades the targets. However, the discerning invader can deceive the defender about its real goal so that it can improve its performance. On the other hand, the defender tries to confront the invader by guessing its true goal. A two-level policy is obtained via reinforcement learning (RL). In the lower level, the invader and the defender learn their optimal policies to invade or defend a particular territory. In the higher level, the invader learns which territory it should pretend to invade in order to manipulate the defender's belief function. A multiagent reinforcement learning (MARL) algorithm is implemented for obtaining the optimal policies via the minimax Q-learning algorithm at the lower level. Whereas for the higher-level policy a single-agent Q-learning algorithm is utilized. Results of different reward functions are compared. The results show that the invader can improve its performance by taking advantage of deception.