Deep Reinforcement Learning: An Overview

TL;DR

This paper provides a comprehensive overview of recent advances in deep reinforcement learning, covering core concepts, mechanisms, and diverse applications across multiple domains.

Contribution

It offers a structured summary of recent progress in deep RL, highlighting key elements, mechanisms, and applications, serving as a valuable resource for researchers.

Findings

Deep RL has achieved notable success in games like AlphaGo.

Applications extend to robotics, NLP, computer vision, and more.

The paper summarizes core RL components and mechanisms.

Abstract

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 with background of machine learning, deep learning and reinforcement learning. Next we discuss core RL elements, including value function, in particular, Deep Q-Network (DQN), policy, reward, model, planning, and exploration. After that, we discuss important mechanisms for RL, including attention and memory, unsupervised learning, transfer learning, multi-agent RL, hierarchical RL, and learning to learn. Then we discuss various applications of RL, including games, in particular, AlphaGo, robotics, natural language processing, including dialogue systems, machine translation, and text generation, computer vision, neural architecture design, business management, finance, healthcare, Industry 4.0, smart grid, intelligent transportation systems, and computer systems. We mention topics not reviewed yet, and list a collection of RL resources. After presenting a brief summary, we close with discussions. Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant update.