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

Reinforcement learning methods have recently been very successful at performing complex
sequential tasks like playing Atari games, Go and Poker. These algorithms have …

This work identifies a common flaw of deep reinforcement learning (RL) algorithms: a
tendency to rely on early interactions and ignore useful evidence encountered later …

Deep reinforcement learning (deep RL) combines deep learning and reinforcement
learning, in which artificial agents learn to solve sequential decision-making problems. In the …

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 …

Deep reinforcement learning (RL) algorithms are predominantly evaluated by comparing
their relative performance on a large suite of tasks. Most published results on deep RL …

Deep reinforcement learning has gathered much attention recently. Impressive results were
achieved in activities as diverse as autonomous driving, game playing, molecular …

Deep reinforcement learning (RL) has achieved many recent successes, yet experiment turn-
around time remains a key bottleneck in research and in practice. We investigate how to …

In recent years, significant progress has been made in solving challenging problems across
various domains using deep reinforcement learning (RL). Reproducing existing work and …

Data efficiency is a key challenge for deep reinforcement learning. We address this problem
by using unlabeled data to pretrain an encoder which is then finetuned on a small amount of …