Reconfigurable intelligent surfaces (RISs), also known as intelligent reflecting surfaces
(IRSs), or large intelligent surfaces (LISs), 1 have received significant attention for their …

This paper presents a comprehensive literature review on applications of deep
reinforcement learning (DRL) in communications and networking. Modern networks, eg …

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

The deep reinforcement learning community has made several independent improvements
to the DQN algorithm. However, it is unclear which of these extensions are complementary …

Deep reinforcement learning is the combination of reinforcement learning (RL) and deep
learning. This field of research has been able to solve a wide range of complex …

Abstract We present CURL: Contrastive Unsupervised Representations for Reinforcement
Learning. CURL extracts high-level features from raw pixels using contrastive learning and …

We introduce an exploration bonus for deep reinforcement learning methods that is easy to
implement and adds minimal overhead to the computation performed. The bonus is the error …

This paper reviews exploration techniques in deep reinforcement learning. Exploration
techniques are of primary importance when solving sparse reward problems. In sparse …

In this paper, we describe a phenomenon, which we named “super-convergence”, where
neural networks can be trained an order of magnitude faster than with standard training …

In this chapter, we review the evidence and explanations for the modern productivity
paradox and propose a resolution. Namely, there is no inherent inconsistency between …