Autonomous Tactical Decision-Making for Multi-Aircraft via Reinforcement Learning

In the terminal attack phase, aircraft trajectory planning based only on geometric or dynamic optimal solutions often fails to reflect the tactical intent of the mission which limits the overall effectiveness of penetration operations. To address this problem, this paper embeds tactical intent into the path planning process through tactical template decisions. Structured tactical templates are introduced to guide and constrain maneuver behaviors at a semantic level. This enables the generation of multi-aircraft autonomous penetration strategies driven by tactical intent. The penetration success rate is improved by reducing the average flight altitude and exposure time. Based on this idea, a distributed reinforcement learning–based algorithm is proposed for autonomous tactical template decision-making of multiple aircraft. At the lower level, terminal penetration trajectories and corresponding maneuver parameters are generated under tactical template constraints. At the upper level, a multi-agent reinforcement learning method is used to select the optimal combination of tactical templates based on real-time battlefield information. Extensive simulation experiments in various random battlefield scenarios, as well as comparisons with baseline methods, demonstrate that the proposed approach is effective and robust in producing high-quality tactical decisions in complex and dynamic combat environments.