强化学习在机器人与控制系统中的分类与趋势：一种结构化综述
Taxonomy and Trends in Reinforcement Learning for Robotics and Control Systems: A Structured Review
AI总结：
本文综述了强化学习原理、深度强化学习算法及其在机器人和控制系统中的应用，探讨了核心算法策略及现代DRL技术，旨在连接理论进展与实际应用。
摘要：
强化学习（RL）已成为在动态和不确定环境中使机器人行为智能化的基础方法。本文深入回顾了RL原理、先进的深度强化学习（DRL）算法及其在机器人和控制系统中的整合。从马尔可夫决策过程（MDPs）的形式化开始，研究概述了智能体与环境交互的基本要素，并探讨了核心算法策略，包括演员-评论家方法、基于价值的学习和策略梯度。重点放在现代DRL技术上，如DDPG、TD3、PPO和SAC，这些技术在解决高维连续控制任务中表现出色。引入了结构化的分类法，以跨领域（如运动、操作、多智能体协调和人机交互）对RL应用进行分类，同时涵盖训练方法和部署准备级别。综述整合了最近的研究努力，突出技术趋势、设计模式以及RL在现实世界机器人中的日益成熟。总体而言，本文旨在弥合理论进展与实际应用之间的差距，提供关于RL在自主机器人系统中演变角色的综合视角。
英文摘要：
Reinforcement learning (RL) has become a foundational approach for enabling intelligent robotic behavior in dynamic and uncertain environments. This work presents an in-depth review of RL principles, advanced deep reinforcement learning (DRL) algorithms, and their integration into robotic and control systems. Beginning with the formalism of Markov Decision Processes (MDPs), the study outlines essential elements of the agent-environment interaction and explores core algorithmic strategies including actor-critic methods, value-based learning, and policy gradients. Emphasis is placed on modern DRL techniques such as DDPG, TD3, PPO, and SAC, which have shown promise in solving high-dimensional, continuous control tasks. A structured taxonomy is introduced to categorize RL applications across domains such as locomotion, manipulation, multi-agent coordination, and human-robot interaction, along with training methodologies and deployment readiness levels. The review synthesizes recent research efforts, highlighting technical trends, design patterns, and the growing maturity of RL in real-world robotics. Overall, this work aims to bridge theoretical advances with practical implementations, providing a consolidated perspective on the evolving role of RL in autonomous robotic systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
