Multi-Agent System for Coordinated Defence

Abstract: Today defence systems are becoming more complex as technology advances and it is of great importance to explore new ways of solving problems and keep national defence current. In particular, Artificial Intelligence (AI) is used in an increasing number of industries such as logistic solutions, inventory management and defence. This thesis will evaluate the possibility to use Reinforcement Learning (RL) in an Air Defence Coordination(ADC) scenario at Saab AB. To evaluate RL, a simplified ADC-scenario is considered and solved using two different methods, Q-learning and Deep Q-learning (DQL). The results of the two methods are discussed as well as the limitations in scope and complexity for Q-learning. Deep Q-learning, on the other hand shows to be relatively easy to apply to more complicated scenarios. Finally, one last experiment with a far more complex scenario is constructed in order to show the scalability of DQL and create a foundation for future work in this field.