Reinforcement learning-trained optimisers and Bayesian optimisation for online particle accelerator tuning.

ABSTRACT: Online tuning of particle accelerators is a complex optimisation problem that continues to require manual intervention by experienced human operators. Autonomous tuning is a rapidly expanding field of research, where learning-based methods like Bayesian optimisation (BO) hold great promise in improving plant performance and reducing tuning times. At the same time, reinforcement learning (RL) is a capable method of learning intelligent controllers, and recent work shows that RL can also be used to train domain-specialised optimisers in so-called reinforcement learning-trained optimisation (RLO). In parallel efforts, both algorithms have found successful adoption in particle accelerator tuning. Here we present a comparative case study, assessing the performance of both algorithms while provi

SUBMITTER: Kaiser J 

PROVIDER: S-EPMC11231256 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature