Step into High-Dimensional and Continuous Action Space: A Survey on Applications of Deep Reinforcement Learning to Robotics

被引：0
|
作者：
Duo N. [1 ]
Lü Q. [1 ]
Lin H. [1 ]
Wei H. [1 ]
机构：
[1] Academy of Army Armored Force, Beijing
来源：
Jiqiren/Robot | 2019年 / 41卷 / 02期
关键词：
Deep learning; Reinforcement learning; Robotics;
D O I：
10.13973/j.cnki.robot.180336
中图分类号：
学科分类号：
摘要：
Firstly, the emergence and development of DRL (deep reinforcement learning) are reviewed. Secondly, DRL algorithms used in high-dimensional and continuous action space are classified into value function approximation based algorithms, policy approximation based algorithms and other structures based algorithms. Then, typical DRL algorithms and their characteristics are introduced, especially their ideas, advantages and disadvantages. Finally, the future trends of applying DRL to robotics are forecasted according to the development directions of DRL algorithms. © 2019, Science Press. All right reserved.
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收藏
页码：276 / 288
页数：12
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