Goals play a central role in human cognition. However, computational theories of learning
and decision-making often take goals as given. Here, we review key empirical findings …

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 …

Goal-conditioned reinforcement learning (GCRL), related to a set of complex RL problems,
trains an agent to achieve different goals under particular scenarios. Compared to the …

Building autonomous machines that can explore open-ended environments, discover
possible interactions and build repertoires of skills is a general objective of artificial …

Although the spontaneous origins of concepts from interaction is often given for granted,
how the process can start without a fully developed sensorimotor representation system has …

Notwithstanding the important advances in Artificial Intelligence (AI) and robotics, artificial
agents still lack the necessary autonomy and versatility to properly interact with realistic …

With the rapid advancement of modern society, autonomous systems have been broadly
applied in people's daily lives. Under the guidance of this trend, autonomous vehicles have …

Recent applications of autonomous agents and robots have brought attention to crucial trust-
related challenges associated with the current generation of artificial intelligence (AI) …

A lot of recent machine learning research papers have``open-ended learning''in their title.
But very few of them attempt to define what they mean when using the term. Even worse …

This work presents an architecture that generates curiosity-driven goal-directed exploration
behaviours for an image sensor of a microfarming robot. A combination of deep neural …