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题名: A framework for the robot skill learning using reinforcement learning
作者: Wei YZ(魏英姿) ; Zhao MY(赵明扬)
作者部门: 机器人学研究室
会议名称: 5th International Symposium on Instrumentation and Control Technology
会议日期: October 24-27, 2003
会议地点: BEIJING, China
会议主办者: Natl Nat Sci Fdn China, Chinese Soc Aeronaut & Astronaut, China Instrument & Control Soc, Chinese Soc Astronaut, Chinese Assoc Automat, SPIE, Beijing Univ Aeronaut & Astronaut, Univ Paderborn, Univ Essen, Hiroshima Shudo Univ, Pusan Natl Univ
会议录: FIFTH INTERNATIONAL SYMPOSIUM ON INSTRUMENTATION AND CONTROL TECHNOLOGY
会议录出版者: SPIE-INT SOC OPTICAL ENGINEERING
会议录出版地: BELLINGHAM
出版日期: 2003
页码: 910-914
收录类别: CPCI(ISTP) ; EI
ISSN号: 0277-786X
ISBN号: 0-8194-5137-1
关键词: reinforcement learning ; robot skill ; learning control ; reward function ; CMAC neural network
摘要: Robot acquiring skill is a process similar to human skill learning. Reinforcement learning (RL) is an on-line actor critic method for a robot to develop its skill. The reinforcement function has become the critical component for its effect of evaluating the action and guiding the learning process. We present an augmented reward function that provides a new way for RL controller to incorporate prior knowledge and experience into the RL controller. Also, the difference form of augmented reward function is considered carefully. The additional reward beyond conventional reward will provide more heuristic information for RL. In this paper, we present a strategy for the task of complex skill learning. Automatic robot shaping policy is to dissolve the complex skill into a hierarchical learning process. The new form of value function is introduced to attain smooth motion switching swiftly. We present a formal, but practical, framework for robot skill learning and also illustrate with an example the utility of method for learning skilled robot control on line.
语种: 英语
产权排序: 1
内容类型: 会议论文
URI标识: http://ir.sia.cn/handle/173321/8474
Appears in Collections:机器人学研究室_会议论文

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Recommended Citation:
魏英姿; 赵明扬.A framework for the robot skill learning using reinforcement learning.见:SPIE-INT SOC OPTICAL ENGINEERING.FIFTH INTERNATIONAL SYMPOSIUM ON INSTRUMENTATION AND CONTROL TECHNOLOGY,BELLINGHAM,2003,910-914
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