发表机构
University of Technology Nuremberg; Max Planck Institute for Intelligent Systems; University of Alberta; ETH Zürich; University of California San Diego（纽伦堡工业大学; 马克斯·普朗克智能系统研究所; 阿尔伯塔大学; 苏黎世联邦理工学院; 加州大学圣迭戈分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本教程围绕强化学习与控制理论的联系与差异，介绍自适应控制、演员-评论家强化算法及二者结合的新方法，助力跨领域专家理解对方工具方法。
AI 中文摘要
强化学习与控制理论是两个相邻的科学领域，均聚焦于利用反馈优化未知动态系统的控制器，二者共同根源为动态规划，但已发展出不同的方法、目标与文化。尽管数十年间相互影响，两个领域间仍存在显著鸿沟。本教程介绍自适应控制、演员-评论家强化算法，以及将这两种范式结合用于经典运动控制问题数据驱动决策的新方法，旨在为理解两种方法的核心差异提供基础，帮助各领域专家更好地理解并运用对方的工具与方法。
英文摘要
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.
CommentsINFORMS tutorial
