通过人类知识、基于模型和无模型方法实现多阶段机器人操纵技能学习的高效强化学习方法

IF 5.9 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Xing Liu, Zihao Liu, Gaozhao Wang, Zhengxiong Liu, Panfeng Huang
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引用次数: 0

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Efficient Reinforcement Learning Method for Multi-Phase Robot Manipulation Skill Acquisition via Human Knowledge, Model-Based, and Model-Free Methods
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来源期刊
IEEE Transactions on Automation Science and Engineering
IEEE Transactions on Automation Science and Engineering 工程技术-自动化与控制系统
CiteScore
12.50
自引率
14.30%
发文量
404
审稿时长
3.0 months
期刊介绍: The IEEE Transactions on Automation Science and Engineering (T-ASE) publishes fundamental papers on Automation, emphasizing scientific results that advance efficiency, quality, productivity, and reliability. T-ASE encourages interdisciplinary approaches from computer science, control systems, electrical engineering, mathematics, mechanical engineering, operations research, and other fields. T-ASE welcomes results relevant to industries such as agriculture, biotechnology, healthcare, home automation, maintenance, manufacturing, pharmaceuticals, retail, security, service, supply chains, and transportation. T-ASE addresses a research community willing to integrate knowledge across disciplines and industries. For this purpose, each paper includes a Note to Practitioners that summarizes how its results can be applied or how they might be extended to apply in practice.
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