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 ORCID:

Hao-nan Wang

https://orcid.org/0000-0002-0792-3858

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Frontiers of Information Technology & Electronic Engineering  2020 Vol.21 No.12 P.1726-1744

http://doi.org/10.1631/FITEE.1900533


Deep reinforcement learning: a survey


Author(s):  Hao-nan Wang, Ning Liu, Yi-yun Zhang, Da-wei Feng, Feng Huang, Dong-sheng Li, Yi-ming Zhang

Affiliation(s):  Science and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, Changsha 410000, China

Corresponding email(s):   wanghaonan14@nudt.edu.cn, liuning17a@nudt.edu.cn, zhangyiyun213@163.com, fengdawei@nudt.edu.cn, huangfeng@nudt.edu.cn, dsli@nudt.edu.cn, zhangyiming@nudt.edu.cn

Key Words:  Reinforcement learning, Deep reinforcement learning, Reinforcement learning applications


Hao-nan Wang, Ning Liu, Yi-yun Zhang, Da-wei Feng, Feng Huang, Dong-sheng Li, Yi-ming Zhang. Deep reinforcement learning: a survey[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(12): 1726-1744.

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doi="10.1631/FITEE.1900533"
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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 model-based 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.

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
国防科技大学并行与分布处理国家重点实验室,中国长沙市,410000

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。

关键词:强化学习;深度强化学习;强化学习应用

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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