Collaborating with humans requires rapidly adapting to their individual strengths,
weaknesses, and preferences. Unfortunately, most standard multi-agent reinforcement …

A wide range of reinforcement learning (RL) problems---including robustness, transfer
learning, unsupervised RL, and emergent complexity---require specifying a distribution of …

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

Abstract Progress in Reinforcement Learning (RL) algorithms goes hand-in-hand with the
development of challenging environments that test the limits of current methods. While …

Standard deep reinforcement learning algorithms use a shared representation for the policy
and value function, especially when training directly from images. However, we argue that …

A key challenge for reinforcement learning (RL) consists of learning in environments with
sparse extrinsic rewards. In contrast to current RL methods, humans are able to learn new …

Inattentional blindness is the psychological phenomenon that causes one to miss things in
plain sight. It is a consequence of the selective attention in perception that lets us remain …

Deep reinforcement learning (RL) agents often fail to generalize beyond their training
environments. To alleviate this problem, recent work has proposed the use of data …

Existing approaches for improving generalization in deep reinforcement learning (RL) have
mostly focused on representation learning, neglecting RL-specific aspects such as …

Interaction and cooperation with humans are overarching aspirations of artificial intelligence
research. Recent studies demonstrate that AI agents trained with deep reinforcement …