Model-based reinforcement learning methods often use learning only for the purpose of
estimating an approximate dynamics model, offloading the rest of the decision-making work …

The study of zero-shot generalisation (ZSG) in deep Reinforcement Learning (RL) aims to
produce RL algorithms whose policies generalise well to novel unseen situations at …

The two fields of machine learning and graphical causality arose and are developed
separately. However, there is, now, cross-pollination and increasing interest in both fields to …

Abstract 'Intuitive physics' enables our pragmatic engagement with the physical world and
forms a key component of 'common sense'aspects of thought. Current artificial intelligence …

Through multi-agent competition, the simple objective of hide-and-seek, and standard
reinforcement learning algorithms at scale, we find that agents create a self-supervised …

Particle physics is a branch of science aiming at discovering the fundamental laws of matter
and forces. Graph neural networks are trainable functions which operate on graphs—sets of …

Unsupervised object-centric learning aims to represent the modular, compositional, and
causal structure of a scene as a set of object representations and thereby promises to …

Finding accurate solutions to partial differential equations (PDEs) is a crucial task in all
scientific and engineering disciplines. It has recently been shown that machine learning …

The problem of common sense remains a major obstacle to progress in artificial intelligence.
Here, we argue that common sense in humans is founded on a set of basic capacities that …

The cornerstone of neural algorithmic reasoning is the ability to solve algorithmic tasks,
especially in a way that generalises out of distribution. While recent years have seen a surge …