Multi-Invader Multi-Defender Differential Game Using Reinforcement Learning

This paper addresses a game of guarding a territory with several invaders and several defenders. Although there are a plethora of studies for the single-invader single-defender scenario, or single-invader multi-defender case, there are few articles on the multi-invader case. The reason is the assignment problem. Each defender must know the policy for capturing each invader. In addition, each defender must choose an invader to capture during the game. We proposed a hierarchical reinforcement learning (HRL) process to solve the assignment problem in a multi-invader multi-defender game of guarding a territory. In the proposed method, a higher-level policy is able to change the assignment in the middle of the game as the game environment is changing. The proposed method is also examined on the game of active target defence. It is shown that the proposed method can solve the assignment problem without any knowledge of an external optimal policy, such as geometric approaches.