Data-Efficient and Robust Reinforcement Learning for Moving Devices

Övrig text i vetenskaplig tidskrift, 2026

One main conclusion in this paper is that model-based RL is much more data-efficient, but also more robust against neglected high-frequency dynamics. A modularized model-based RL strategy is therefore proposed where a nonlinear state-space model is estimated. In this model some minor physical knowledge can be easily introduced. Combining feedback and feedforward control with temporal optimization based on the estimated model, it is shown that energy and peak power for moving devices can be significantly reduced, utilizing much less data compared to standard model-free RL.