Learning Families of Fixed-Wing Aircraft Control Policies with Hypernetwork-Conditioned Reinforcement Learning
This paper investigates whether it is more effective to condition reinforcement learning policies across changing dynamics using standard input augmentation or hypernetwork-based parameter modulation. We use a small fixed-wing uncrewed aircraft under actuator failures as a demanding test case, as these failures introduce distinctly different closed-loop dynamics. We evaluate parameter-efficient hypernetwork formulations based on Feature-wise Linear Modulation (FiLM) and Low-Rank Adaptation (LoRA), trained using proximal policy optimization. High-fidelity six-degree-of-freedom simulations demonstrate that hypernetwork-conditioned policies improve robustness compared to standard multilayer perceptron policies trained with augmented inputs. Most notably, the hypernetwork policies generalize effectively to time-varying actuator failure modes not encountered during training, indicating that modulating internal parameters provides significant advantages over standard input augmentation for this class of highly coupled dynamics.