Accelerator Control using Gaussian Process Model Predictive Control Based Reinforcement Learning

<!--HTML-->Abstract: This project aims to develop a model-based Reinforcement Learning (RL) algorithm, based on the GP-MPC(Gaussian Process-Model Predictive Control) approach, for controlling accelerator systems using Machine Learning. By leveraging insufficient data and uncertainty quantification capabilities of Gaussian Processes, the algorithm will overcome limitations related to beam instrumentation and training large iterations. The final outcome will be the integration of the modified algorithm into CERN's Generic Optimisation Framework, facilitating easy deployment for operational scenarios and enabling intelligent accelerator control.