无线电通信技术2024，Vol.50Issue(5)：831-842,12.DOI:10.3969/j.issn.1003-3114.2024.05.001
基于离线强化学习的研究综述
Survey of Research on Offline Reinforcement Learning
摘要
Abstract
Offline reinforcement learning,as an emerging paradigm,leverages a vast amount of offline data for learning without the need of active interactions with the environment.It demonstrates high potential and value,especially in high-risk fields such as health-care and autonomous driving.This review will sequentially unfold from the basic concepts of offline reinforcement learning,core issues,main methods,and focus on introducing various strategies to mitigate distributional shift.These include constraining target policy and behavior policy alignment,value function constraints,quantification of model uncertainty,and model-based offline reinforcement learn-ing methods.Finally,the article discusses current simulation environments for offline reinforcement learning and significant application scenarios.关键词
强化学习/离线强化学习/自动决策/外推误差Key words
reinforcement learning/offline reinforcement learning/automated decision-making/extrapolation errors分类
电子信息工程引用本文复制引用
陈锶奇,耿婕,汪云飞,余伟驰,赵佳宁,王仕超..基于离线强化学习的研究综述[J].无线电通信技术,2024,50(5):831-842,12.基金项目
国家自然科学基金(61602391) （61602391）
天津市科技计划项目(22JCZDJC00580) National Natural Science Foundation of China(61602391) （22JCZDJC00580）
Tianjin Science and Technology Plan Project(22JCZDJC00580) （22JCZDJC00580）
