State of the art deep reinforcement learning algorithms take many millions of interactions to
attain human-level performance. Humans, on the other hand, can very quickly exploit highly …

Episodic memory-based methods can rapidly latch onto past successful strategies by a non-
parametric memory and improve sample efficiency of traditional reinforcement learning …

Deep reinforcement learning methods attain super-human performance in a wide range of
environments. Such methods are grossly inefficient, often taking orders of magnitudes more …

The principle of``optimism in the face of uncertainty''underpins many theoretically successful
reinforcement learning algorithms. In this paper we provide a general framework for …

Reinforcement learning (RL) algorithms have made huge progress in recent years by
leveraging the power of deep neural networks (DNN). Despite the success, deep RL …

In recent years, a number of reinforcement learning (RL) methods have been pro-posed to
explore complex environments which differ across episodes. In this work, we show that the …

In many environments only a tiny subset of all states yield high reward. In these cases, few of
the interactions with the environment provide a relevant learning signal. Hence, we may …

As humans, our goals and our environment are persistently changing throughout our lifetime
based on our experiences, actions, and internal and external drives. In contrast, typical …

In recent years deep reinforcement learning (RL) systems have attained superhuman
performance in a number of challenging task domains. However, a major limitation of such …

Strong worst-case performance bounds for episodic reinforcement learning exist but
fortunately in practice RL algorithms perform much better than such bounds would predict …