Speaker
Description
Reinforcement learning (RL) is a unique learning
paradigm inspired by the behaviour of animals and humans to learn to solve tasks autonomously. Learning occurs
through interactions with an environment, exploring, and
evaluating strategies under various conditions. RL excels in
complex environments, can handle delayed consequences,
and is able to learn solely from experience without access
to an explicit model of the system. This makes RL particularly promising for particle accelerators, where the dynamic
conditions of particle beams and accelerator systems require
continuous adaptation, and modelling is challenging. Although RL applications are emerging in accelerator physics
and showing promising results, their widespread introduction faces critical challenges. Among the main obstacles
are the effective formulation of control problems, training,
and the deployment of solutions in real systems. In this talk I will present the promise and open challenges of RL in particle accelerators.
