3 papers
cs.LG2026
ARLBench: Flexible and Efficient Benchmarking for Hyperparameter Optimization in Reinforcement Learning
Jannis Becktepe, Julian Dierkes, Carolin Benjamins +7
Hyperparameters are a critical factor in reliably training well-performing reinforcement learning (RL) agents. Unfortunately, developing and evaluating automated approaches for tun…
cs.LG2026
Practitioner Motives to Use Different Hyperparameter Optimization Methods
Niclas KannengieÃer, Niklas Hasebrook, Felix Morsbach +5
Programmatic hyperparameter optimization (HPO) methods, such as Bayesian optimization and evolutionary algorithms, are highly sample-efficient in identifying optimal hyperparameter…
cs.LG2025
Automated Machine Learning for Remaining Useful Life Predictions
Marc-André Zöller, Fabian Mauthe, Peter Zeiler +2
Being able to predict the remaining useful life (RUL) of an engineering system is an important task in prognostics and health management. Recently, data-driven approaches to RUL pr…