Tutorial on Meta-Reinforcement Learning and GP-MPC at the RL4AA'24 Workshop: Tutorial on Meta-Reinforcement Learning and GP-MPC at the RL4AA'24 Workshop

In the forefront of particle physics research, autonomous accelerators represent a paradigm shift towards more efficient and precise control mechanisms. At the 2024 Reinforcement Learning for Autonomous Accelerators (RL4AA) Workshop, this tutorial unveils novel applications of Meta-Reinforcement Learning (Meta-RL) and Gaussian Process Model Predictive Control (GP-MPC), showcased through simulations of the Advanced Wakefield Experiment (AWAKE). Meta-RL empowers systems to learn from limited data, adapting to new operational scenarios rapidly. GP-MPC integrates the robustness of Gaussian Processes in handling uncertainties with the foresight of Model Predictive Control, crucial for the high-stakes precision required in accelerator operations.

Structured to cater to both newcomers and seasoned researchers, the tutorial will dissect the theoretical frameworks underpinning Meta-RL and GP-MPC, followed by an immersive application phase on AWAKE simulation models. Highlights include:

An introductory overview of Meta-Reinforcement Learning and Gaussian Process Model Predictive Control in the context of autonomous accelerators.
Detailed exploration of applying Meta-RL for adaptive control strategies in dynamic and uncertain accelerator environments.
In-depth discussion on leveraging GP-MPC for optimizing accelerator performance, emphasizing uncertainty management and real-time decision-making.
Hands-on sessions on the integration of Meta-RL and GP-MPC in AWAKE simulations, showcasing enhanced control and adaptability.

Participants will depart with a solid grasp of Meta-RL and GP-MPC techniques, ready to apply these advanced control strategies in the domain of autonomous accelerators. This tutorial aims to illuminate the path towards more autonomous, efficient, and precise accelerator technologies through the synergistic use of adaptive learning and model predictive control.