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Updated: Jul 24, 2025

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基于深度强化学习的物体运输使用任务空间分解

Gyuho Eoh1

  • 1Department of Mechatronics Engineering, Tech University of Korea, 237 Sangidaehak-ro, Siheung-si 15073, Gyeonggi-do, Republic of Korea.

Sensors (Basel, Switzerland)
|July 11, 2023
PubMed
概括

本研究引入了一种新的深度强化学习 (DRL) 方法,使用任务空间分解 (TSD) 进行机器人物体运输. 这种方法使机器人能够在不需要重新学习的情况下在复杂的环境中导航,克服了以前的DRL方法的局限性.

关键词:
深度强化学习的学习.物体运输物体运输物体运输物体任务空间的分解

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