The reinforcement learning (RL) research area is very active, with an important number of
new contributions, especially considering the emergent field of deep RL (DRL). However, a …

This editorial article introduces the Frontiers Research Topic and Electronic Book (eBook)
on Intrinsic Motivations (IMs), which involved the publication of 24 articles with the journals …

Curiosity-driven learning is foundational to human cognition. By enabling humans to
autonomously decide when and what to learn, curiosity has been argued to be crucial for …

Humans are autotelic agents who learn by setting and pursuing their own goals. However,
the precise mechanisms guiding human goal selection remain unclear. Learning progress …

Bodily movement has long been employed as a foundation for cultivating mental skills such
as attention, self-control or mindfulness, with recent studies documenting the positive …

In this paper, we present goal-discovering robotic architecture for intrisically-motivated
learning (GRAIL), a four-level architecture that is able to autonomously: 1) discover changes …

Learning with an objective to minimize the mismatch with a reference distribution has been
shown to be useful for generative modeling and imitation learning. In this paper, we …

Learning about many things can provide numerous benefits to a reinforcement learning
system. For example, learning many auxiliary value functions, in addition to optimizing the …

Despite its successes, to date Artificial Intelligence (AI) is still characterized by a number of
shortcomings with regards to different application domains and goals. These limitations are …

Machine learning researchers are beginning to understand the need for machines to be
able to self-assess their competence and express it in a human understandable form …