Meta Policy Switching for Secure UAV Deconfliction in Adversarial Airspace

TL;DR

This paper introduces a meta-policy switching framework using Thompson sampling for UAV navigation that adaptively selects among multiple robust policies to defend against unknown adversarial attacks, enhancing safety and reliability.

Contribution

It proposes a novel meta-policy switching method with a DTS mechanism for adaptive policy selection, improving robustness against unseen adversarial attacks in UAV navigation.

Findings

Significantly improved navigation efficiency under adversarial attacks.

Higher conflict-free trajectory rates compared to baseline methods.

Theoretical guarantees on regret minimization and antifragile behavior.

Abstract

Autonomous UAV navigation using reinforcement learning (RL) is vulnerable to adversarial attacks that manipulate sensor inputs, potentially leading to unsafe behavior and mission failure. Although robust RL methods provide partial protection, they often struggle to generalize to unseen or out-of-distribution (OOD) attacks due to their reliance on fixed perturbation settings. To address this limitation, we propose a meta-policy switching framework in which a meta-level polic dynamically selects among multiple robust policies to counter unknown adversarial shifts. At the core of this framework lies a discounted Thompson sampling (DTS) mechanism that formulates policy selection as a multi-armed bandit problem, thereby minimizing value distribution shifts via self-induced adversarial observations. We first construct a diverse ensemble of action-robust policies trained under varying perturbation intensities. The DTS-based meta-policy then adaptively selects among these policies online, optimizing resilience against self-induced, piecewise-stationary attacks. Theoretical analysis shows that the DTS mechanism minimizes expected regret, ensuring adaptive robustness to OOD attacks and exhibiting emergent antifragile behavior under uncertainty. Extensive simulations in complex 3D obstacle environments under both white-box (Projected Gradient Descent) and black-box (GPS spoofing) attacks demonstrate significantly improved navigation efficiency and higher conflict free trajectory rates compared to standard robust and vanilla RL baselines, highlighting the practical security and dependability benefits of the proposed approach.