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 …

Humans and animals use mental representations of the spatial structure of the world to
navigate. The classical view is that these representations take the form of Euclidean …

Modeling human cognition is challenging because there are infinitely many mechanisms
that can generate any given observation. Some researchers address this by constraining the …

The fields of neuroscience and artificial intelligence (AI) have a long and intertwined history.
In more recent times, however, communication and collaboration between the two fields has …

A cognitive map has long been the dominant metaphor for hippocampal function, embracing
the idea that place cells encode a geometric representation of space. However, evidence for …

Recent breakthroughs in artificial intelligence (AI) have enabled machines to plan in tasks
previously thought to be uniquely human. Meanwhile, the planning algorithms implemented …

Highlights•Multiple scales of learning (and hence meta-learning) are ubiquitous in
nature.•Many existing lines of work in neuroscience and cognitive science touch upon …

Abstract Representation learning and option discovery are two of the biggest challenges in
reinforcement learning (RL). Proto-value functions (PVFs) are a well-known approach for …

One of the most striking features of human cognition is the ability to plan. Two aspects of
human planning stand out—its efficiency and flexibility. Efficiency is especially impressive …

This paper introduces an active inference formulation of planning and navigation. It
illustrates how the exploitation–exploration dilemma is dissolved by acting to minimise …