通过Lyapunov不确定性控制实现可靠的离线增强学习
IEEE transactions on neural networks and learning systems
|October 14, 2025
概括
本研究介绍了Lyapunov不确定性控制 (LUC),一种新的离线强化学习 (RL) 方法. LUC限制了代理的状态空间,以确保可靠的决策并减少预先收集的数据集中的不确定性.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 控制理论 控制理论
背景情况:
- 线下强化学习 (RL) 面临的挑战是由于预先收集的数据集的不确定性.
- 现有的方法在不确定的环境中难以保证可靠的政策学习.
研究的目的:
- 提出一种新的线下RL方法,以应对不确定性挑战.
- 通过减少贝尔曼不确定性来确保可靠的决策.
主要方法:
- 灵感来自于利亚普诺夫的稳定性和控制不变集.
- 引入了一个受限制的状态空间,用于代理操作.
- 调节预期的贝尔曼不确定性以保持低不确定性.
主要成果:
- 拟议的方法,Lyapunov不确定性控制 (LUC),保证代理人在低不确定性状态的围内保持.
- 对于学习模型,证明了贝尔曼不确定性的降低.
- 确保贝尔曼不确定性的增长趋势保持在可接受的范围内.
结论:
- LUC有效地解决了线下RL中的不确定性.
- 该方法确保了可靠的政策学习和决策.
- 广泛的分析证实了LUC的有效性和可行性.
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