As materials researchers increasingly embrace machine-learning (ML) methods, it is natural
to wonder what lessons can be learned from other fields undergoing similar developments …

Reinforcement Learning (RL) is a machine learning (ML) technique to learn sequential
decision-making in complex problems. RL is inspired by trial-and-error based human/animal …

Sample efficiency and exploration remain major challenges in online reinforcement learning
(RL). A powerful approach that can be applied to address these issues is the inclusion of …

The incredible feats of athleticism demonstrated by humans are made possible in part by a
vast repertoire of general-purpose motor skills, acquired through years of practice and …

Recent work has shown that offline reinforcement learning (RL) can be formulated as a
sequence modeling problem (Chen et al., 2021; Janner et al., 2021) and solved via …

Deep reinforcement learning (RL) has emerged as a promising approach for autonomously
acquiring complex behaviors from low-level sensor observations. Although a large portion of …

Reinforcement learning is a learning paradigm for solving sequential decision-making
problems. Recent years have witnessed remarkable progress in reinforcement learning …

In this tutorial article, we aim to provide the reader with the conceptual tools needed to get
started on research on offline reinforcement learning algorithms: reinforcement learning …

Efficiently navigating a superpressure balloon in the stratosphere requires the integration of
a multitude of cues, such as wind speed and solar elevation, and the process is complicated …

Robotic equipment has been playing a central role since the proposal of smart
manufacturing. Since the beginning of the first integration of industrial robots into production …