通过利用具有历史优势的政策与绩效监管来强化强化学习
IEEE transactions on neural networks and learning systems
|March 13, 2026
概括
强化强化学习 (RL) 方法提高了针对新型攻击的政策稳定性. 霍普利用历史上最佳的政策和适应机制来增强代理防御能力和性能.
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:
- 强化学习 (RL) 中现有的对抗训练方法对新型攻击的稳定性有限.
- 当前的RL方法通常依赖于简单的试错,使它们易受复杂的对抗性干扰的影响.
研究的目的:
- 通过利用历史最佳政策与绩效监管 (HORP) 引入一种新的方法,通过利用历史最佳政策与绩效监管 (HORP) 引入强大的RL,以提高RL的政策强度.
- 开发一种能够对各种国家攻击进行普遍防御的RL代理.
主要方法:
- 霍普利用历史最佳政策来指导政策优化,并产生各种各样的对抗性干扰.
- 通过考虑优先学习的价值差距和政策分配差异来构建指导价值函数.
- 适应性性能意识的优化机制和动态扰动度调制用于提高稳定性.
主要成果:
- 与现有方法相比,HORP在自然性能和对各种国家攻击的稳定性方面都表现出卓越的性能.
- 该方法有效地将学习集中在有前途的行动空间上,并防止代理人偏离最佳性能.
- 实验证实通过可控不确定性注入增强了通用防御能力.
结论:
- 在强化学习的对抗性强度方面,HORP提供了显著的进步.
- 拟议的方法提供了一个更可靠,更强大的RL代理,能够承受新的和多样化的攻击.
- 在安全关键应用中,HORP代表了开发弹性AI系统的有希望的方向.
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