Traditionally, cognitive and computer scientists have viewed intelligence solipsistically, as a
property of unitary agents devoid of social context. Given the success of contemporary …

Deep learning is transforming most areas of science and technology, including electron
microscopy. This review paper offers a practical perspective aimed at developers with …

Reinforcement learning algorithms typically struggle in the absence of a dense, well-shaped
reward function. Intrinsically motivated exploration methods address this limitation by …

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

When observing the outcome of a choice, people are sensitive to the choice's context, such
that the experienced value of an option depends on the alternatives: getting $1 when the …

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 …

Abstract Generative Artificial Intelligence (AI) is one of the most exciting developments in
Computer Science of the last decade. At the same time, Reinforcement Learning (RL) has …

The objective of a reinforcement learning agent is to behave so as to maximise the sum of a
suitable scalar function of state: the reward. These rewards are typically given and …

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 …

Humans learn to master open-ended repertoires of skills by imagining and practicing their
own goals. This autotelic learning process, literally the pursuit of self-generated (auto) goals …