论文标题
连续环境中的时间分化学习
Temporal-Differential Learning in Continuous Environments
论文作者
论文摘要
在本文中，引入了一种新的增强学习（RL）方法，称为时间差异方法。与传统的时间差异学习方法相比，它在为连续环境开发新型的RL技术中起着至关重要的作用。特别是，开发了连续的最小二乘策略评估（CT-LSPE）和连续的时间差异（CT-TD）学习方法。提供了理论和经验证据，以证明所提出的时间差异学习方法的有效性。
In this paper, a new reinforcement learning (RL) method known as the method of temporal differential is introduced. Compared to the traditional temporal-difference learning method, it plays a crucial role in developing novel RL techniques for continuous environments. In particular, the continuous-time least squares policy evaluation (CT-LSPE) and the continuous-time temporal-differential (CT-TD) learning methods are developed. Both theoretical and empirical evidences are provided to demonstrate the effectiveness of the proposed temporal-differential learning methodology.
