打通强化学习与控制理论的桥梁，助力数据驱动决策
Foundations of Reinforcement Learning and Control:Connections and New Perspectives
- 融合自适应控制与演员-评论家算法，统一两种范式
- 在经典运动控制问题上实现高效数据驱动决策
- 帮助两个领域的专家理解彼此工具与方法
强化学习与控制理论是两个关注通过反馈优化未知动态系统控制器的相邻科学领域。尽管两者均源于动态规划，但发展出不同的方法、目标和文化。历经数十年相互影响，两领域间仍存在显著差距。本文教程介绍自适应控制、演员-评论家强化学习算法，并提出一种新方法将二者结合，用于经典运动控制问题的数据驱动决策。旨在为理解两种方法的核心差异提供基础，并帮助各领域专家更好理解与应用对方的工具与方法。
原文摘要 · Abstract (English)
Reinforcement learning and control theory are two adjacent scientific fields that focus on optimizing the controller of unknown dynamical systems using feedback. While both fields have common roots in dynamic programming, they have evolved with distinct methodologies, goals, and cultures. Despite decades of mutual influence, a significant gap persists between the two communities. This tutorial introduces adaptive control, actor-critic reinforcement algorithms, and a new way to combine these two paradigms for data-driven decision making on a classical locomotion control problem. Our aim is to provide a foundation for understanding the core differences between the two approaches and insights to help experts in each field better understand and engage with the tools and approaches of the other.
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