Predictive Lightweight MARL for Resilient Coverage in Sparse-Signaling Aerial Networks

2026-07-27 · Source: cs.MA updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Emerging Technologies & Innovation · Depth: Expert, long

Summary

The Predictive Lightweight Multi-Agent Reinforcement Learning (PL-MARL) framework is proposed to ensure resilient coverage in bandwidth-constrained Unmanned Aerial Vehicle (UAV) swarms. This framework introduces a Kinematic-Aware Inference Engine that proactively reconstructs neighbor trajectories using physical priors, effectively countering coordination collapse caused by sparse signaling and information aging. This approach enables an efficient computation-for-communication trade-off, decoupling structural resilience from signaling frequency. Simulations demonstrate PL-MARL's superior coverage and mission continuity under extreme signaling scarcity, such as 50-second silence intervals, and node failure. It maintains a near-constant inference latency of approximately 0.5 ms, even when scaled to 20 UAVs, and exhibits graceful performance degradation, stabilizing within 10 steps after a node failure.

Key takeaway

For Machine Learning Engineers developing UAV swarm control systems in bandwidth-constrained or interference-prone environments, integrate predictive, physics-guided inference. This enables your systems to proactively reconstruct neighbor states, overcoming information aging and maintaining resilient coverage. It minimizes control overhead, preserving spectrum for payload services and ensuring mission continuity even with extreme signaling scarcity or node failures.

Key insights

Proactive, physics-guided state reconstruction enables robust multi-agent coordination in sparse-signaling, resource-constrained aerial networks.

Principles

• Decouple policy execution from communication frequency.
• Trade redundant cohesion for expanded spatial footprint.
• Computation can strategically replace communication overhead.

Method

PL-MARL uses a Kinematic-Aware Inference Engine to reconstruct neighbor trajectories via physical motion priors. It then applies topology-aware graph attention with positional encoding for decentralized control, trained via centralized MAPPO in a Digital Twin.

In practice

• Implement Kinematic-Aware Inference Engines for UAVs.
• Use topology-aware graph attention for dynamic coordination.
• Prioritize computation over energy-intensive radio transmissions.

Topics

• Multi-Agent Reinforcement Learning
• UAV Swarms
• Kinematic-Aware Inference
• Sparse Signaling Networks
• Resilient Coverage
• Age of Information

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Robotics Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.MA updates on arXiv.org.