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

arXiv cs.AI / 4/1/2026

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Key Points

The paper proposes a robust multi-agent reinforcement learning approach to ensure separation safety for small UAVs when GPS signals are degraded or spoofed.
It models corrupted GPS-derived state observations as a zero-sum game between cooperative agents and an adversary that perturbs observations to maximally reduce safety performance.
The authors derive a closed-form expression for the worst-case adversarial perturbation, avoiding adversarial training and enabling fast (linear-time) evaluation.
They provide accuracy and safety bounds, showing the closed-form perturbation approximates the true worst case with second-order accuracy and that performance degradation grows at most linearly with corruption probability under KL regularization.
In high-density simulation, the integrated “counter-policy” achieves near-zero collision rates for corruption probabilities up to 35%, outperforming a non-adversarially trained baseline.

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.