政策压缩用于低功耗边缘设备的智能连续控制
Thomas Avé1, Tom De Schepper2, Kevin Mets3
1IDLab-Department of Computer Science, University of Antwerp-IMEC, Sint-Pietersvliet 7, 2000 Antwerp, Belgium.
Sensors (Basel, Switzerland)
|August 10, 2024
概括
研究人员开发了一种新的政策蒸方法,以压缩深度强化学习 (DRL) 模型,用于边缘设备上的连续控制任务. 这种技术有效地减少模型大小,同时保持或提高机器人和物联网应用的性能.
科学领域:
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
- 计算机科学 计算机科学
背景情况:
- 深度增强学习 (DRL) 在边缘设备上提供实时推断,增强自动移动机器人 (AMR) 和物联网 (IoT) 设备等应用程序的隐私和可靠性.
- 在功率受限制的边缘设备上部署能源密集型DRL模型具有挑战性,需要模型压缩技术.
- 策略蒸是一种流行的压缩方法,但现有的方法不支持机器人技术中常见的连续行动空间.
研究的目的:
- 调整政策蒸以压缩用于连续控制任务的DLR模型.
- 在压缩过程中保持连续DRL算法的随机性质.
- 为了使得DRL在功率受限制的边缘设备上能够有效地部署,用于现实世界的应用.
主要方法:
- 专门为持续行动空间开发了一种改进的政策蒸方法.
- 专注于保持连续DRL算法的固有随机性.
- 应用该方法来压缩DRL策略用于连续控制任务.
主要成果:
- 成功地将连续控制任务的DRL策略压缩到750%.
- 实现了维持或超过原教师模型能力的41%的绩效水平.
- 在两个流行的连续控制基准测试中表现出有效性.
结论:
- 增强的政策蒸方法有效地压缩了用于连续控制任务的DRL模型.
- 这一进步有助于在资源有限的边缘设备上部署复杂的DRL.
- 该技术显示了提高机器人和物联网中的AI效率和性能的前景.
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