Fueled by recent advances in deep neural networks, reinforcement learning (RL) has been
in the limelight because of many recent breakthroughs in artificial intelligence, including …

A significant body of work on reinforcement learning has been focused on the single-agent
tasks where the agent aims to learn a policy that maximizes the cumulative reward in a …

The advances in reinforcement learning have recorded sublime success in various domains.
Although the multi-agent domain has been overshadowed by its single-agent counterpart …

Abstract Deep Reinforcement Learning (DRL) algorithms have been successfully applied to
a range of challenging control tasks. However, these methods typically suffer from three core …

New edition of the bestselling guide to deep reinforcement learning and how it's used to
solve complex real-world problems. Revised and expanded to include multi-agent methods …

Reinforcement learning (RL) applications, where an agent can simply learn optimal
behaviors by interacting with the environment, are quickly gaining tremendous success in a …

In computer vision and natural language processing, innovations in model architecture that
lead to increases in model capacity have reliably translated into gains in performance. In …

Abstract Deep Reinforcement Learning (DRL) algorithms have been successfully applied to
a range of challenging control tasks. However, these methods typically suffer from three core …

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

We give an overview of recent exciting achievements of deep reinforcement learning (RL).
We discuss six core elements, six important mechanisms, and twelve applications. We start …