摘要
运用加强学习算法解决电梯群控问题往往受限于算法收敛速度慢,很难扩展至具有更高楼层、更多电梯的群控系统.分割状态空间为几个区域,建立具有分割功能的加强学习系统是必要的.所提出的系统结构及其底层工作原理具有普遍意义,可用于解决大状态空间上的最优控制问题,开发了基于群控分区算法的分割模块,运行结果表明了此系统的优势.
It is hard to apply reinforcement learning algorithms to solve elevator group control problem in a building with more floors and elevators. This is mainly because of low convergence speed of reinforcement learning algorithms. It is necessary to partition state space into several regions and establish a reinforcement learning system with partitioning function. The system framework and its inside performance principle have a general significance and can be applied to optimal control problem with large state s...
出处
《上海交通大学学报》
EI
CAS
CSCD
北大核心
2005年第S1期173-177,共5页
Journal of Shanghai Jiaotong University
基金
国家自然科学基金资助(69975013)项目
关键词
分区算法
多智能体系统
加强学习
电梯群控系统
zoning algorithm
multi-agent systems
reinforcement learning
elevator group control system