安全的增强学习与双强度的强度
IEEE transactions on pattern analysis and machine intelligence
|August 15, 2024
概括
本研究引入了强化学习 (RL) 代理的统一框架,使它们能够既安全又强大地应对对抗性干扰. 新的双重政策代方案确保代理人即使在最坏的情况下也保持业绩和安全.
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) 代理容易受到对抗性攻击,影响性能和安全.
- 现有的安全RL和强大的RL方法单独解决这些问题,留下了统一解决方案的空白.
- 挑战在于在竞争条件下平衡可行性和最佳性.
研究的目的:
- 开发一个系统的框架,统一安全和强大的强化学习.
- 解决在对抗性环境中可行性和最佳性的相互交织的挑战.
- 创建一个安全且在对抗性干扰下性能最佳的强化学习代理.
主要方法:
- 问题表述为受约束的两人零和马尔科夫游戏.
- 关于双重政策代方案的建议,以同时优化任务和安全政策.
- 开发一个实用的深度强化学习算法,双强的演员-关键 (DRAC),使用对抗网络.
主要成果:
- 双重政策代方案汇聚到对任务执行和安全的最佳政策.
- DRAC在各种对抗场景 (没有对手,安全对手,性能对手) 中展示了高性能和持续的安全性.
- 在安全关键的基准指标上,DRAC显著优于现有的基准方法.
结论:
- 拟议的框架有效地统一了安全和强大的强化学习.
- DRAC提供了一个实用和有效的解决方案,用于开发在敌对环境中既安全又强大的代理.
- 这项工作推动了安全关键应用中可靠的人工智能系统的开发.
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