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 …

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 …

Building models that can be rapidly adapted to novel tasks using only a handful of annotated
examples is an open challenge for multimodal machine learning research. We introduce …

Learning representations of algorithms is an emerging area of machine learning, seeking to
bridge concepts from neural networks with classical algorithms. Several important works …

Psychologists and neuroscientists extensively rely on computational models for studying
and analyzing the human mind. Traditionally, such computational models have been hand …

Making good decisions requires thinking ahead, but the huge number of actions and
outcomes one could consider makes exhaustive planning infeasible for computationally …

Strong inductive biases give humans the ability to quickly learn to perform a variety of tasks.
Although meta-learning is a method to endow neural networks with useful inductive biases …

We develop a conceptual framework for studying collective adaptation in complex socio-
cognitive systems, driven by dynamic interactions of social integration strategies, social …

While often seeming to investigate rather different problems, research into value-based
decision making and cognitive control have historically offered parallel insights into how …

Humans have the fascinating ability to achieve goals in a complex and constantly changing
world, still surpassing modern machine-learning algorithms in terms of flexibility and …