强化学习及其在机器人任务规划中的进展与分析  

Reinforcement Learning and Its Application in Robot Task Planning:A Survey

作　　者：张晓明 高士杰 姚昌瑀 褚誉 彭硕 ZHANG Xiaoming;GAO Shijie;YAO Changyu;CHU Yu;PENG Shuo(Institute of Physical Science and Information Technology,Anhui University,Hefei 230601)

机构地区：[1]安徽大学物质科学与信息技术研究院,合肥230601

出　　处：《模式识别与人工智能》2023年第10期902-917,共16页Pattern Recognition and Artificial Intelligence

基　　金：安徽省自然科学基金项目(No.2208085MF174);教育部中国高校产学研创新基金项目(No.2021ZYA06004)资助。

摘　　要：强化学习可以让机器人通过与环境的交互,学习最优的行动策略,是目前机器人领域关注的重要前沿方向之一.文中简述机器人任务规划问题的形式化建模,分析强化学习的主要方法,分别介绍无模型强化学习、基于模型的强化学习和分层强化学习的研究进展,着重探讨基于强化学习的机器人任务规划的研究进展,并讨论各种强化学习及其应用情况.最后总结强化学习在机器人应用中面临的问题与挑战,展望未来的研究方向.Reinforcement learning enables robots learn the optimal action policy through the interaction with the environment,representing an important frontier direction in the field of robotics.In this paper,the formal modeling of robot task planning problem is briefly introduced,and the main methods of reinforcement learning are analyzed,including model-free reinforcement learning,model-based reinforcement learning and hierarchical reinforcement learning.The research progress in robot task planning based on reinforcement learning is explored.Various reinforcement learning methods and their applications are discussed as well.Finally,the key problems of reinforcement learning in robot applications are summarized,and the future research directions are prospected.

关 键 词：机器人 任务规划 强化学习 无模型强化学习 基于模型的强化学习 分层强化学习 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]