According to cognitive psychology and related disciplines, the development of complex
problem-solving behaviour in biological agents depends on hierarchical cognitive …

Intrinsically motivated spontaneous exploration is a key enabler of autonomous
developmental learning in human children. It enables the discovery of skill repertoires …

Many reinforcement learning (RL) environments consist of independent entities that interact
sparsely. In such environments, RL agents have only limited influence over other entities in …

It has been a long-standing dream to design artificial agents that explore their environment
efficiently via intrinsic motivation, similar to how children perform curious free play. Despite …

Hierarchical reinforcement learning (HRL) holds great potential for sample-efficient learning
on challenging long-horizon tasks. In particular, letting a higher level assign subgoals to a …

To successfully tackle challenging manipulation tasks, autonomous agents must learn a
diverse set of skills and how to combine them. Recently, self-supervised agents that set their …

Cognitive Psychology and related disciplines have identified several critical mechanisms
that enable intelligent biological agents to learn to solve complex problems. There exists …

Reward-based optimization algorithms require both exploration, to find rewards, and
exploitation, to maximize performance. The need for efficient exploration is even more …

In recent years, reinforcement learning (RL) has become increasingly successful in its
application to the quantum domain and the process of scientific discovery in general …

Although model-based and model-free approaches to learning the control of systems have
achieved impressive results on standard benchmarks, generalization to task variations is still …