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Deep reinforcement learning: a survey

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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 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.

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  1. Science and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, Changsha, 410000, China

    Hao-nan Wang, Ning Liu, Yi-yun Zhang, Da-wei Feng, Feng Huang, Dong-sheng Li & Yi-ming Zhang

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Hao-nan WANG designed the research. Ning LIU and Yi-yun ZHANG collected the literature. Hao-nan WANG drafted the manuscript. Ning LIU, Da-wei FENG, and Feng HUANG helped organize the manuscript. Hao-nan WANG and Ning LIU revised the manuscript. Hao-nan WANG finalized the paper under the guidance of Dong-sheng LI and Yi-ming ZHANG.

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Hao-nan WANG, Ning LIU, Yi-yun ZHANG, Da-wei FENG, Feng HUANG, Dong-sheng LI, and Yi-ming ZHANG declare that they have no conflict of interest.

Project supported by the National Natural Science Foundation of China (Nos. 61772541, 61872376, and 61932001)

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Wang, Hn., Liu, N., Zhang, Yy. et al. Deep reinforcement learning: a survey. Front Inform Technol Electron Eng 21, 1726–1744 (2020). https://doi.org/10.1631/FITEE.1900533

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