Harnessing the Power of Gradient-Based Simulations for Multi-Objective Optimization in Particle Accelerators

TL;DR

This paper introduces a differentiable reinforcement learning approach for multi-objective optimization in particle accelerators, outperforming traditional methods like genetic algorithms and Bayesian optimization in complex, constrained scenarios.

Contribution

It demonstrates the effectiveness of deep differentiable reinforcement learning for multi-objective optimization in particle accelerators, leveraging a physics-based differentiable surrogate model.

Findings

DDRL outperforms MFRL, BO, and GA in high-dimensional problems.

The approach effectively handles strict local and global constraints.

Results are presented as Pareto fronts for two objectives.

Abstract

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.