Robust Multi-Agent Reinforcement Learning for Small UAS Separation Assurance under GPS Degradation and Spoofing

// abstract

We address robust separation assurance for small Unmanned Aircraft Systems (sUAS) under GPS degradation and spoofing via Multi-Agent Reinforcement Learning (MARL). In cooperative surveillance, each aircraft (or agent) broadcasts its GPS-derived position; when such position broadcasts are corrupted, the entire observed air traffic state becomes unreliable. We cast this state observation corruption as a zero-sum game between the agents and an adversary: with probability R, the adversary perturbs the observed state to maximally degrade each agent's safety performance. We derive a closed-form expression for this adversarial perturbation, bypassing adversarial training entirely and enabling linear-time evaluation in the state dimension. We show that this expression approximates the true worst-case adversarial perturbation with second-order accuracy. We further bound the safety performance gap between clean and corrupted observations, showing that it degrades at most linearly with the corruption probability under Kullback-Leibler regularization. Finally, we integrate the closed-form adversarial policy into a MARL policy gradient algorithm to obtain a robust counter-policy for the agents. In a high-density sUAS simulation, we observe near-zero collision rates under corruption levels up to 35%, outperforming a baseline policy trained without adversarial perturbations.

// highlights

• Casts GPS corruption as a zero-sum game between the agents and an adversary that perturbs the observed state with probability R.
• Derives a closed-form worst-case perturbation — no adversarial training, linear-time in the state dimension, accurate to second order.
• Proves the clean-vs-corrupted safety gap grows at most linearly with corruption probability under KL regularization.
• Integrates the closed-form adversary into a MARL policy-gradient algorithm, achieving near-zero collisions under up to 35% corruption.

// bibtex

@misc{zongo2026robust,
  title         = {Robust Multi-Agent Reinforcement Learning for Small UAS Separation
                   Assurance under GPS Degradation and Spoofing},
  author        = {Alex Zongo and Filippos Fotiadis and Ufuk Topcu and Peng Wei},
  year          = {2026},
  eprint        = {2603.28900},
  archivePrefix = {arXiv}
}

// cite