具有一般函数近似的对抗模仿学习:理论分析和实际算法
IEEE transactions on pattern analysis and machine intelligence
|January 26, 2026
概括
本研究介绍了基于优化的对抗模拟学习 (OPT-AIL),以进行有效的在线学习,并进行复杂函数近似. OPT-AIL实现了有效的近专家政策,弥合了模仿学习中的理论与实践的差距.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
- 强化学习是一种强化学习.
背景情况:
- 敌对模仿学习 (AIL) 通过神经网络展示了实际的成功.
- 对AIL的理论分析仅限于简化的设置,造成了理论与实践之间的差距.
- 现有的AIL方法通常涉及复杂的设计,阻碍实际应用.
研究的目的:
- 在对抗模仿学习中弥合理论和实践之间的差距.
- 开发基于理论的在线AIL方法,用于一般函数近似.
- 引入一个新的框架,基于优化的AIL (OPT-AIL),以实现高效的政策学习.
主要方法:
- 引入了基于优化的AIL (OPT-AIL) 框架.
- 开发了无模型和基于模型的OPT-AIL变体.
- 利用在线优化用于奖励学习和乐观规范化优化用于政策学习.
主要成果:
- 这两种OPT-AIL变体都实现了多项式专家样本和相互作用复杂性.
- 在一般函数近似下证明有效的对抗模仿学习.
- 经验结果表明,OPT-AIL的表现优于最先进的深度AIL方法.
结论:
- OPT-AIL提供了第一个可证明有效的对抗模拟学习方法,用于一般函数近似.
- 该框架通过要求对两个目标进行近似优化来简化实际实施.
- OPT-AIL通过将理论保障与实际可用性统一,在该领域取得了重大进展.
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