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 …

In this article, we aim to provide a literature review of different formulations and approaches
to continual reinforcement learning (RL), also known as lifelong or non-stationary RL. We …

Interactive Natural Language Processing (iNLP) has emerged as a novel paradigm within
the field of NLP, aimed at addressing limitations in existing frameworks while aligning with …

Interactive Imitation Learning in Robotics: A Survey Page 1 Interactive Imitation Learning in
Robotics: A Survey Page 2 Other titles in Foundations and Trends® in Robotics A Survey on …

Planning in realistic environments requires searching in large planning spaces. Affordances
are a powerful concept to simplify this search, because they model what actions can be …

Most current decision-making research focuses on classical economic scenarios, where
choice offers are prespecified and where action dynamics play no role in the decision …

The central theme of this review is the dynamic interaction between information selection
and learning. We pose a fundamental question about this interaction: How do we learn what …

Goal-conditioned hierarchical reinforcement learning (HRL) is a promising approach for
scaling up reinforcement learning (RL) techniques. However, it often suffers from training …

Deep reinforcement learning~(RL) has achieved remarkable successes in complex single-
task settings. However, designing RL agents that can learn multiple tasks and leverage prior …

Researchers across cognitive, neuro-and computer sciences increasingly reference 'human-
like'artificial intelligence and 'neuroAI'. However, the scope and use of the terms are often …