Generalization to out-of-distribution (OOD) data is a capability natural to humans yet
challenging for machines to reproduce. This is because most learning algorithms strongly …

In the past couple of years, the interest of the fluid mechanics community for deep
reinforcement learning techniques has increased at fast pace, leading to a growing …

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 …

We introduce four new real-world distribution shift datasets consisting of changes in image
style, image blurriness, geographic location, camera operation, and more. With our new …

Learning from visual observations is a fundamental yet challenging problem in
Reinforcement Learning (RL). Although algorithmic advances combined with convolutional …

Abstract We introduce Procgen Benchmark, a suite of 16 procedurally generated game-like
environments designed to benchmark both sample efficiency and generalization in …

Reinforcement learning methods trained on few environments rarely learn policies that
generalize to unseen environments. To improve generalization, we incorporate the inherent …

While successful for various computer vision tasks, deep neural networks have shown to be
vulnerable to texture style shifts and small perturbations to which humans are robust. In this …

While agents trained by Reinforcement Learning (RL) can solve increasingly challenging
tasks directly from visual observations, generalizing learned skills to novel environments …

Learning generalizable policies that can adapt to unseen environments remains challenging
in visual Reinforcement Learning (RL). Existing approaches try to acquire a robust …