Reinforcement Learning (RL) is a machine learning (ML) technique to learn sequential
decision-making in complex problems. RL is inspired by trial-and-error based human/animal …

Synaptic plasticity, the activity-dependent change in neuronal connection strength, has long
been considered an important component of learning and memory. Computational and …

Most existing benchmarks for grounding language in interactive environments either lack
realistic linguistic elements, or prove difficult to scale up due to substantial human …

Intelligent agents need to generalize from past experience to achieve goals in complex
environments. World models facilitate such generalization and allow learning behaviors …

Planning has been very successful for control tasks with known environment dynamics. To
leverage planning in unknown environments, the agent needs to learn the dynamics from …

Deep reinforcement learning (RL) methods have driven impressive advances in artificial
intelligence in recent years, exceeding human performance in domains ranging from Atari to …

In an effort to overcome limitations of reward-driven feature learning in deep reinforcement
learning (RL) from images, we propose decoupling representation learning from policy …

Neuroscience research is undergoing a minor revolution. Recent advances in machine
learning and artificial intelligence research have opened up new ways of thinking about …

The emergence of powerful artificial intelligence (AI) is defining new research directions in
neuroscience. To date, this research has focused largely on deep neural networks trained …

Owing to their ability to both effectively integrate information over long time horizons and
scale to massive amounts of data, self-attention architectures have recently shown …