摘要
土石方调配问题是水利水电工程设计和施工中一个重要问题,传统解决方法包括线性规划、大系统分解协调、动态规划等,存在一定的局限性。本文尝试运用强化学习中离散型Q学习的方法解决土石方调配问题。文中探讨了土石方调配问题Q学习模型的构建及解法,并通过一个工程实例,验证了本文提出方法的可行性。为后续利用强化学习方法解决动态土石方调配平衡的研究奠定了基础。
Earthwork allocation is an important issue in the design and construction of water conservancy and hydropower projects, and traditional methods, such as linear programming, large-scale system decomposition and coordination, and dynamic programming, have some limitations in practice. This paper explores a new method of using discrete Q-learning in reinforcement learning to solve the problems of earthwork allocation. We discuss the construction and solution of a Q-learning model for earthwork allocation problems and verify its feasibility through the analysis of an engineering example. This work would lay a basis for further studies on the balance of dynamic earthwork allocation using reinforcement learning.
作者
王仁超
李宗蔚
WANG Renchao;LI Zongwei(State Key Laboratory of Hydraulic Engineering Simulation, Tianjin University, Tianjin 300350)
出处
《水力发电学报》
EI
CSCD
北大核心
2019年第6期11-18,共8页
Journal of Hydroelectric Engineering
关键词
水利水电工程
土石方调配
强化学习
离散型Q学习
water conservancy and hydropower project
earthwork allocation
reinforcement learning
discrete Q-learning
