Author

Stefan Huber

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Preprint Jul 2026

Integrating Physics-Informed Neural Networks for Safe Reinforcement Learning in a 1-DoF Helicopter System

This work-in-progress paper embeds a differentiable physics model directly into the proximal policy optimization (PPO) actor loss function, by simulating short-horizon future trajectories during training, to reduce constraint violations while maintaining reliable target tracking.

George Schafer, Jakob Rehrl, Stefan Huber · 0 citations

Preprint Jul 2026

Safe Reinforcement Learning using Ideas from Model Predictive Control

A generalized framework that combines the adaptive, high-performance nature of deep reinforcement learning (DRL) with the formal safety guarantees of model predictive control (MPC) is proposed, demonstrating successful exploration and stable policy convergence on physical hardware.

George Schafer, Jakob Rehrl, Stefan Huber et al. · 0 citations