An Asymmetric Dual-Graph Actor–Critic for Scheduling Heterogeneous Directed-Energy Systems in Counter-UAV Defense

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Coordinated swarms of low-cost commercial unmanned aerial vehicles (UAVs) pose a growing security threat to civil infrastructure, public events, and restricted airspace. Effective counter-UAV defense requires the real-time, cooperative scheduling of heterogeneous directed-energy systems, where high-energy lasers provide precise single-target engagement and high-power microwaves provide wide-area suppression. To address this threat, we formulate the defensive scheduling problem as a partially observable Markov game and propose the Heterogeneous Dual-Graph Multi-Agent Actor–Critic (HDG-MAAC). HDG-MAAC employs two graphs: a fixed-size local graph encoded by a graph attention network (GAT) actor for decentralized threat selection and a shared global graph with three semantic edge types encoded by a graph convolutional network (GCN) critic for stable credit assignment. This asymmetric design is tailored to decision-making and evaluation, respectively. In a two-system, four-target simulation benchmark with direct, zig-zag, and feint maneuver patterns, HDG-MAAC converges stably across eight seeds, with a final-stage return of 33.6±4.0, and neutralizes 2.79±0.26 targets over 1000 deterministic episodes, outperforming DDPG (2.20), MADDPG (2.13), and H2G-MAAC (1.64) with p<0.01. In analytically tractable geometric scenarios, the rule-based scheduler with exact geometric pointing still yields more raw interceptions. The learning method’s contribution is therefore limited to training stability, interpretability, and applicability beyond such scenarios, rather than the raw interception count. Ablation studies indicate that this robustness originates from the GCN critic, whose contribution lies in training stability rather than in raising the final performance ceiling. An attention analysis yields an overall target association rate of 42.0%, with 47.7% and 47.1% for direct and zig-zag maneuvers, respectively, compared to a 25% chance baseline, as well as a drop to 27.1% under feints. Without an explicit coordination reward, coordinated target convergence emerges in 34% of firing frames. HDG-MAAC provides a stable and interpretable decision support framework for civil security operators safeguarding public infrastructure from rogue drone groups. The framework is intended solely for protective civilian use, with responsible use considerations.