神经网络压缩用于强化学习任务
Dmitry A Ivanov1,2, Denis A Larionov3,4, Oleg V Maslennikov5
1Lomonosov Moscow State University, Moscow, Russia.
Scientific reports
|March 22, 2025
概括
这项研究优化了强化学习 (RL) 通过神经网络修剪和量子化,显著减少模型大小,以实现高效的硬件部署. 这些技术提高了能源效率,降低了延迟,并在现实世界RL应用中提高了吞吐量.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 机器人技术 机器人技术 机器人技术
背景情况:
- 现实世界的强化学习 (RL) 应用,如机器人,需要低延迟,节能,高通量神经网络推断.
- 精度和修剪是优化神经网络推断,提高能源效率,延迟和吞吐量的既定方法.
研究的目的:
- 在流行的RL算法中系统地研究修剪和定量化技术的应用,以优化神经网络.
- 确定这些优化方法在硬件部署的RL任务中的适用性限制.
主要方法:
- 这项研究将修剪和量子化应用到深度Q网络 (DQN) 和软演员批评 (SAC) 算法.
- 在各种RL环境中进行了实验,包括MuJoCo和Atari.
主要成果:
- 实现了神经网络大小减少多达400倍.
- 证明了修剪和量化对优化RL推断的有效性.
结论:
- 修剪和量子化对于优化RL中的神经网络是有效的,使硬件部署成为可能.
- 这些优化可以减少功耗和延迟,同时增加RL系统的吞吐量.
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