Deep Meta Reinforcement Learning for Rapid Adaptation In Linear Markov Decision Processes: Applications to CERN’s AWAKE Project

Real-world applications of reinforcement learning (RL) face challenges such as the need for numerous interactions and achieving stable training under dynamic conditions. Meta-RL emerges as a solution, particularly in environments where simulations cannot perfectly mimic real-world conditions. This study demonstrates Meta-RL’s potential in the CERN’s AWAKE project, focusing on the electron line’s control. By incorporating Model-Agnostic Meta-Learning (MAML), we showcase how Meta-RL facilitates rapid adaptation to environmental changes with minimal interaction steps. Our findings indicate Meta-RL’s efficacy in managing Partially Observable Markov Decision Processes (POMDPs) with evolving hidden parameters, underlining its significance in high-dimensional control challenges prevalent in particle physics experiments and beyond.