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. DOI: 10.13973/j.cnki.robot.210008
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. DOI: 10.13973/j.cnki.robot.210008
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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. DOI: 10.13973/j.cnki.robot.210008
Citation: CHEN Jiapan, ZHENG Minhua. A Survey of Robot Manipulation Behavior Research Based on Deep Reinforcement Learning[J]. ROBOT, 2022, 44(2): 236-256. DOI: 10.13973/j.cnki.robot.210008
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基于深度强化学习的机器人操作行为研究综述

A Survey of Robot Manipulation Behavior Research Based on Deep Reinforcement Learning

    摘要
  • 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。

     

    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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