7 papers · 1 filter
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…
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…
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…
Instance Selection for Dynamic Algorithm Configuration with Reinforcement Learning: Improving Generalization
Carolin Benjamins, Gjorgjina Cenikj, Ana Nikolikj +3
Dynamic Algorithm Configuration (DAC) addresses the challenge of dynamically setting hyperparameters of an algorithm for a diverse set of instances rather than focusing solely on i…
Position: A Call to Action for a Human-Centered AutoML Paradigm
Marius Lindauer, Florian Karl, Anne Klier +6
Automated machine learning (AutoML) was formed around the fundamental objectives of automatically and efficiently configuring machine learning (ML) workflows, aiding the research o…
Structure in Deep Reinforcement Learning: A Survey and Open Problems
Aditya Mohan, Amy Zhang, Marius Lindauer
Reinforcement Learning (RL), bolstered by the expressive capabilities of Deep Neural Networks (DNNs) for function approximation, has demonstrated considerable success in numerous a…