深度强化学习综述

王浩楠; 刘苧; 章艺云; 冯大伟; 黄峰; 李东升; 张一鸣

doi:10.1631/FITEE.1900533

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深度强化学习综述

常规文章 | Updated：2022-05-19

- 深度强化学习综述
- Deep reinforcement learning: a survey
- 信息与电子工程前沿（英文） 2020年21卷第12期页码：1726-1744
- Affiliations：
  
  Science and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, Changsha 410000, China
- Author bio：
  
  Hao-nan WANG, E-mail: wanghaonan14@nudt.edu.cn
  [ "Ning LIU, liuning17a@nudt.edu.cn" ]
  [ "Yi-yun ZHANG, zhangyiyun213@163.com" ]
  [ "Da-wei FENG, fengdawei@nudt.edu.cn" ]
  [ "Feng HUANG, huangfeng@nudt.edu.cn" ]
  [ "Dong-sheng LI, dsli@nudt.edu.cn" ]
  [ "Yi-ming ZHANG, zhangyiming@nudt.edu.cn" ]
- Funds：
  
  Project supported by the National Natural Science Foundation of China (Nos. 61772541, 61872376, and 61932001)
- DOI：10.1631/FITEE.1900533
  中图分类号： TP18
- 收稿：2019-09-29，
  
  修回：2020-;6-;4，
  
  网络出版：2020-10-15，
  
  纸质出版：2020-12
- Accepted：
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王浩楠, 刘苧, 章艺云, 等. 深度强化学习综述[J]. 信息与电子工程前沿（英文）, 2020,21(12):1726-1744.

Hao-nan WANG, Ning LIU, Yi-yun ZHANG, et al. Deep reinforcement learning: a survey[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(12): 1726-1744.
王浩楠, 刘苧, 章艺云, 等. 深度强化学习综述[J]. 信息与电子工程前沿（英文）, 2020,21(12):1726-1744. DOI： 10.1631/FITEE.1900533.

Hao-nan WANG, Ning LIU, Yi-yun ZHANG, et al. Deep reinforcement learning: a survey[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(12): 1726-1744. DOI： 10.1631/FITEE.1900533.

摘要

深度强化学习已成为人工智能研究中最受欢迎的主题之一，已被广泛应用于端到端控制、机器人控制、推荐系统、自然语言对话系统等多个领域。本文对深度强化学习算法和应用进行系统分类，提供详细论述，并将现有深度强化学习算法分为基于模型的方法、无模型方法和高级深度强化学习方法。之后，全面分析探索、逆强化学习和迁移强化学习等高级算法的进展。最后，概述当前深度强化学习的代表性应用，并分析4个亟待解决的问题。

Abstract

Deep reinforcement learning (RL) has become one of the most popular topics in artificial intelligence research. It has been widely used in various fields

such as end-to-end control

robotic control

recommendation systems

and natural language dialogue systems. In this survey

we systematically categorize the deep RL algorithms and applications

and provide a detailed review over existing deep RL algorithms by dividing them into modelbased methods

model-free methods

and advanced RL methods. We thoroughly analyze the advances including exploration

inverse RL

and transfer RL. Finally

we outline the current representative applications

and analyze four open problems for future research.

关键词

Keywords

references

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