相关实验视频
Updated: Jun 21, 2025

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New Variations for Strategy Set-shifting in the Rat
Published on: January 23, 2017
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线下奖励扰乱促进了在线RL中的分配转移
Zishun Yu1, Siteng Kang1, Xinhua Zhang1
1Department of Computer Science, University of Illinois Chicago, Chicago, IL, USA.
概括
本研究介绍了一种隐蔽的数据中毒攻击,针对线下到线上强化学习 (RL). 这种新的方法利用了批评者规范化的RL漏洞,在使用最小的数据进行在线微调时导致显著的性能下降.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 强化学习是一种强化学习.
背景情况:
- 线下到线上强化学习 (RL) 通过利用事先收集的线下数据来减少在线样本的复杂性.
- 这种方法引入了针对线下培训阶段的数据中毒攻击的新漏洞.
研究的目的:
- 揭示了批评者规范化的线下RL在新型数据中毒攻击中的漏洞.
- 提出一种隐蔽攻击方法,在线训练期间保持性能,但降低在线微调.
主要方法:
- 利用双层优化技术.
- 在线下线上RL框架内促进高估和分配转移.
- 开发一个小预算和没有白盒访问的隐形攻击.
主要成果:
- 展示了隐蔽性:离线训练性能保持不变.
- 在线微调阶段观察到显著的性能下降.
- 在四个实验环境中证实了攻击的有效性.
结论:
- 关键规范的线下到线上RL易受隐蔽的数据中毒攻击的影响.
- 拟议的双层优化方法有效地利用了这些漏洞.
- 攻击是实用的,需要最小的资源和没有白盒访问.
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