In order to improve reproducibility, deep reinforcement learning (RL) has been adopting
better scientific practices such as standardized evaluation metrics and reporting. However …

Optimizing expensive-to-evaluate black-box functions of discrete (and potentially
continuous) design parameters is a ubiquitous problem in scientific and engineering …

The fields of both Natural Language Processing (NLP) and Automated Machine Learning
(AutoML) have achieved remarkable results over the past years. In NLP, especially Large …

Reinforcement Learning (RL), bolstered by the expressive capabilities of Deep Neural
Networks (DNNs) for function approximation, has demonstrated considerable success in …

In this work, we propose algorithms and methods that enable learning dexterous object
manipulation using simulated one-or two-armed robots equipped with multi-fingered hand …

Abstract Population Based Training (PBT) is an efficient hyperparameter optimization
algorithm. PBT is a single-objective algorithm, but many real-world hyperparameter …

Although Reinforcement Learning (RL) has shown to be capable of producing impressive
results, its use is limited by the impact of its hyperparameters on performance. This often …

Reinforcement Learning (RL), bolstered by the expressive capabilities of Deep Neural
Networks (DNNs) for function approximation, has demonstrated considerable success in …

The increasing availability of graph-structured data motivates the task of optimising over
functions defined on the node set of graphs. Traditional graph search algorithms can be …

Hyperparameter optimization plays a key role in the machine learning domain. Its
significance is especially pronounced in reinforcement learning (RL), where agents …