Model-free and Bayesian Ensembling Model-based Deep Reinforcement Learning for Particle Accelerator Control Demonstrated on the FERMI FEL. - Simon Hirlaender; Niky Bruchon (CoRR, 2020)

An Online Iterative Linear Quadratic Approach for a Satisfactory Working Point Attainment at FERMI. - Niky Bruchon; Gianfranco Fenu; Giulio Gaio; Simon Hirlaender; Marco Lonza; Felice Andrea Pellegrino; Erica Salvato (Inf., 2021)

Deep Q-Learning versus Proximal Policy Optimization: Performance Comparison in a Material Sorting Task. - Reuf Kozlica; Stefan Wegenkittl; Simon Hirländer (ISIE, 2023)

A Modular Test Bed for Reinforcement Learning Incorporation into Industrial Applications. - Reuf Kozlica; Georg Schäfer; Simon Hirländer; Stefan Wegenkittl (CoRR, 2023)

Towards automatic setup of 18 MeV electron beamline using machine learning. - Francesco Velotti; Brennan Goddard; Verena Kain; Rebecca Louise Ramjiawan; Giovanni Zevi Della Porta; Simon Hirlaender (Mach. Learn. Sci. Technol., 2023)

Comparison of Model Predictive Control and Proximal Policy Optimization for a 1-DOF Helicopter System. - Georg Schäfer; Jakob Rehrl; Stefan Huber 001; Simon Hirlaender (INDIN, 2024)

Python-Based Reinforcement Learning on Simulink Models. - Georg Schäfer; Maximilian Schirl; Jakob Rehrl; Stefan Huber 001; Simon Hirlaender (CoRR, 2024)

The Crucial Role of Problem Formulation in Real-World Reinforcement Learning. - Georg Schäfer; Tatjana Krau; Jakob Rehrl; Stefan Huber 001; Simon Hirlaender (ICPS, 2025)

Ph.D. Forum: Multi-Agent Reinforcement Learning in Wireless Network Communication. - Sabrina Pochaba; Peter Dorfinger; Matthias Herlich; Roland Kwitt; Simon Hirlaender (WoWMoM, 2025)

Multi-Objective Reinforcement Learning for Energy-Efficient Industrial Control. - Georg Schäfer; Raphael Seliger; Jakob Rehrl; Stefan Huber 001; Simon Hirlaender (CoRR, 2025)