Recurrent neural networks (RNNs) trained with machine learning techniques on cognitive
tasks have become a widely accepted tool for neuroscientists. In this short opinion piece, we …

Reinforcement learning is an essential paradigm for solving sequential decision problems
under uncertainty. Despite many remarkable achievements in recent decades, applying …

What can be learned about causality and experimentation from passive data? This question
is salient given recent successes of passively-trained language models in interactive …

One of the fundamental cognitive abilities of humans is to quickly resolve uncertainty by
generating hypotheses and testing them via active trials. Encountering a novel phenomenon …

Reinforcement Learning (RL), bolstered by the expressive capabilities of Deep Neural
Networks (DNNs) for function approximation, has demonstrated considerable success in …

While Reinforcement Learning (RL) has made great strides towards solving increasingly
complicated problems, many algorithms are still brittle to even slight environmental changes …

Board, card or video games have been played by virtually every individual in the world.
Games are popular because they are intuitive and fun. These distinctive qualities of games …

Developing interactive software, such as websites or games, is a particularly engaging way
to learn computer science. However, teaching and giving feedback on such software is time …

Current domain-independent, classical planners require symbolic models of the problem
domain and instance as input, resulting in a knowledge acquisition bottleneck. Meanwhile …

We present XLand-MiniGrid, a suite of tools and grid-world environments for meta-
reinforcement learning research inspired by the diversity and depth of XLand and the …