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任务分解和基于奖励系统的强化学习算法,用于选择和放置

Byeongjun Kim1, Gunam Kwon1, Chaneun Park2

  • 1Department of Electronic Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea.

Biomimetics (Basel, Switzerland)
|June 27, 2023
PubMed
概括

本研究介绍了一个强化学习算法,用于机器人挑选和放置任务. 该方法将任务分解为子任务,在模拟中达到93.2%的成功率.

关键词:
选择和放置的选择和放置.柔软的演员 - 批评家深度强化学习的学习.机器人操纵器 机器人操纵器任务分解 分解.

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