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Unmanned aerial vehicle (UAV) swarm cooperative decision-making has attracted increasing attentions because of its low-cost, reusable, and distributed characteristics. However, existing non-learning-based methods rely on small-scale, known scenarios, and cannot solve complex multi-agent cooperation problem in large-scale, uncertain scenarios. This paper proposes a hierarchical multi-agent reinforcement learning (HMARL) method to solve the heterogeneous UAV swarm cooperative decision-making problem for the typical suppression of enemy air defense (SEAD) mission, which is decoupled into two sub-problems, i.e., the higher-level target allocation (TA) sub-problem and the lower-level cooperative attacking (CA) sub-problem. A HMARL agent model, consisting of a multi-agent deep Q network (MADQN) based TA agent and multiple independent asynchronous proximal policy optimization (IAPPO) based CA agents, is established. MADQN-TA agent can dynamically adjust the TA schemes according to the relative position. To encourage exploration and promote learning efficiency, the Metropolis criterion and inter-agent information exchange techniques are introduced. IAPPO-CA agent adopts independent learning paradigm, which can easily scale with the number of agents. Comparative simulation results validate the effectiveness, robustness, and scalability of the proposed method.

Unmanned Aerial Vehicle Swarm Cooperative Decision-Making for SEAD Mission: A Hierarchical Multiagent Reinforcement Learning Approach