Reinforcement Learning-Based MAS Interception in Antagonistic Environments
Siqing Sun, Defu Cai, Hai-Tao Zhang, Ning Zhe Xing · IEEE/CAA Journal of Automatica Sinica · 2024
Dear Editor, As a promising multi-agent systems (MASs) operation, autonomous interception has attracted more and more attentions in these years, where defenders prevent intruders from reaching destinations. So far, most of the relevant methods are applied in ideal environments without agent damages. As a remedy, this letter proposes a more realistic interception method for MASs suffered by damages, where the defenders are fewer than the intruders. Firstly, a multiagent interception frame (MAIF) is proposed, enabling the defenders to take actions and interact with the environments. To address non-stationarity issue induced by MAIF, a multi-agent reinforcement learning-based interception method (MAIM) is developed by sophisticatedly designing a reward function. Sufficient conditions are derived to guarantee the convergence of MAIM. Finally, numerical simulations are conducted to verify the effectiveness of the proposed method.