| 基于深度强化学习的机器人操作行为研究综述 |
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| 引用本文： | 陈佳盼,郑敏华.基于深度强化学习的机器人操作行为研究综述[J].机器人,2022,44(2):236-256. |
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| 作者姓名： | 陈佳盼 郑敏华 |
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| 作者单位： | 1. 北京交通大学机械与电子控制工程学院, 北京 100044;2. 载运工具先进制造与测控技术教育部重点实验室(北京交通大学), 北京 100044 |
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| 基金项目： | 中央高校基本科研业务费专项资金（2019RC028）；;国家自然科学基金（51705016）； |
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| 摘 要： | 通过梳理、总结前人的研究，首先对深度学习和强化学习的基本理论和算法进行介绍，进而对深度强化学习的流行算法和在机器人操作领域的应用现状进行综述。最后，根据目前存在的问题及解决方法，对深度强化学习在机器人操作领域未来的发展方向作出总结与展望。
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| 关 键 词： | 深度学习 强化学习 机器人操作 深度强化学习 机器人学习 |
| 收稿时间： | 2021-01-04 |
A Survey of Robot Manipulation Behavior Research Based on Deep Reinforcement Learning |
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| CHEN Jiapan,ZHENG Minhua.A Survey of Robot Manipulation Behavior Research Based on Deep Reinforcement Learning[J].Robot,2022,44(2):236-256. |
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| Authors: | CHEN Jiapan ZHENG Minhua |
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| Affiliation: | 1. School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China;2. Key Laboratory of Vehicle Advanced Manufacturing, Measuring and Control Technology (Beijing Jiaotong University), Ministry of Education, Beijing 100044, China |
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| Abstract: | By summarizing previous studies,the basic theories and algorithms of deep learning and reinforcement learning are introduced firstly.Secondly,the popular DRL (deep reinforcement learning) algorithms and their applications to robot manipulation are summarized.Finally,the future development directions of applying DRL to robot manipulation are forecasted according to the current problems and possible solutions. |
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| Keywords: | deep learning reinforcement learning robot manipulation deep reinforcement learning robot learning |
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