Robust Adaptive Control of Chaotic Systems via Hybrid Reinforcement Learning and Gaussian Process Regression

**Abstract:** This paper presents a novel adaptive control strategy for chaotic systems exhibiting uncertainty and nonlinearity, leveraging a hybrid reinforcement learning (RL) and Gaussian process regression (GPR) framework. Unlike conventional control methods struggling with high-dimensionality and parametric uncertainties, our approach dynamically adapts to system changes through the integration of an RL agent optimizing control policies and a GPR model predicting system dynamics. The resultant control system demonstrates robust stability and enhanced performance across a range of chaotic system configurations, exhibiting potential for applications in areas such as aerospace engineering and power grid stabilization. This system is immediately commercializable due to its reliance on established technologies.