Speaker
Description
Particle accelerator operation requires simultaneous optimization of
multiple objectives. Multi-Objective Optimization (MOO) is particularly challenging
due to trade-offs between the objectives. Evolutionary algorithms, such as genetic
algorithm (GA), have been leveraged for many optimization problems, however, they
do not apply to complex control problems by design. This paper demonstrates
the power of differentiability for solving MOO problems using a Deep Differentiable
Reinforcement Learning (DDRL) algorithm in particle accelerators. We compare
DDRL algorithm with Model Free Reinforcement Learning (MFRL), GA and Bayesian
Optimization (BO) for simultaneous optimization of heat load and trip rates in the
Continuous Electron Beam Accelerator Facility (CEBAF). The underlying problem
enforces strict constraints on both individual states and actions as well as cumulative
(global) constraint for energy requirements of the beam. A physics-based surrogate
model based on real data is developed. This surrogate model is differentiable and allows
back-propagation of gradients. The results are evaluated in the form of a Pareto-front
for two objectives. We show that the DDRL outperforms MFRL, BO, and GA on high
dimensional problems.
