Stable Modular Control via Contraction Theory for Reinforcement Learning

no code implementations • 7 Nov 2023 • Bing Song, Jean-Jacques Slotine, Quang-Cuong Pham

We propose a novel way to integrate control techniques with reinforcement learning (RL) for stability, robustness, and generalization: leveraging contraction theory to realize modularity in neural control, which ensures that combining stable subsystems can automatically preserve the stability.

reinforcement-learning Reinforcement Learning (RL)