Meta-path guided policy distillation for resilient coordination in autonomous unmanned swarm
Year: 2025
Type: article
Abstract: Enhancing the resilience of Autonomous Unmanned Swarms (AUS) requires policies that remain effective under severe, structured disruptions while respecting the heterogeneous semantics of inter-subsystem interactions. Existing reinforcement learning (RL) approaches typically aggregate first-order neighborhoods in a path-agnostic manner, thereby blurring typed, ordered, and directed multi-hop dependencies encoded by domain meta-paths. We propose MPGPD-RC, a Meta- Path Guided Policy Distillation framework for Resilient Coordination that couples: (i) meta-path-guided embeddings learned by path-specific graph attention with contrastive reconstruction and attention fusion, and (ii) a teacher-student scheme in which a PPO teacher trained with a relaxed meta-path mask provides trajectories, and a student aligns both action distributions (KL) and trajectory-level structural codes via path-aware contrastive learning. Empirical evaluations validate that MPGPD-RC consistently surpasses state-of-the-art baselines across diverse perturbation scenarios by modeling complex, high-order dependencies that underpin resilient coordination.
Source: PLoS ONE
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,382
OpenAlex ID: W7117662113