33 citations · 47 across the 6 of their papers we have counts for
9 papers · 1 filter
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…
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…
Squirrel: A Switching Hyperparameter Optimizer
Noor Awad, Gresa Shala, Difan Deng +9
In this short note, we describe our submission to the NeurIPS 2020 BBO challenge. Motivated by the fact that different optimizers work well on different problems, our approach swit…
Sample-Efficient Automated Deep Reinforcement Learning
Jörg K. H. Franke, Gregor Köhler, André Biedenkapp +1
Despite significant progress in challenging problems across various domains, applying state-of-the-art deep reinforcement learning (RL) algorithms remains challenging due to their…