91 citations · 301 across the 27 of their papers we have counts for
8 papers · 1 filter
CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning
Carolin Benjamins, Theresa Eimer, Frederik Schubert +4
While Reinforcement Learning has made great strides towards solving ever more complicated tasks, many algorithms are still brittle to even slight changes in their environment. This…
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
Marius Lindauer, Katharina Eggensperger, Matthias Feurer +6
Algorithm parameters, in particular hyperparameters of machine learning algorithms, can substantially impact their performance. To support users in determining well-performing hype…
TempoRL: Learning When to Act
André Biedenkapp, Raghu Rajan, Frank Hutter +1
Reinforcement learning is a powerful approach to learn behaviour through interactions with an environment. However, behaviours are usually learned in a purely reactive fashion, whe…
Self-Paced Context Evaluation for Contextual Reinforcement Learning
Theresa Eimer, André Biedenkapp, Frank Hutter +1
Reinforcement learning (RL) has made a lot of advances for solving a single problem in a given environment; but learning policies that generalize to unseen variations of a problem…
DACBench: A Benchmark Library for Dynamic Algorithm Configuration
Theresa Eimer, André Biedenkapp, Maximilian Reimer +3
Dynamic Algorithm Configuration (DAC) aims to dynamically control a target algorithm's hyperparameters in order to improve its performance. Several theoretical and empirical result…
Bag of Baselines for Multi-objective Joint Neural Architecture Search and Hyperparameter Optimization
Julia Guerrero-Viu, Sven Hauns, Sergio Izquierdo +7
Neural architecture search (NAS) and hyperparameter optimization (HPO) make deep learning accessible to non-experts by automatically finding the architecture of the deep neural net…
On the Importance of Hyperparameter Optimization for Model-based Reinforcement Learning
Baohe Zhang, Raghu Rajan, Luis Pineda +5
Model-based Reinforcement Learning (MBRL) is a promising framework for learning control in a data-efficient manner. MBRL algorithms can be fairly complex due to the separate dynami…
In-Loop Meta-Learning with Gradient-Alignment Reward
Samuel Müller, André Biedenkapp, Frank Hutter
At the heart of the standard deep learning training loop is a greedy gradient step minimizing a given loss. We propose to add a second step to maximize training generalization. To…