强化学习在基于多主体模型决策支持系统中的应用 ——以湖泊水环境决策支持系统为例

利用研究复杂系统和多主体(multi-agent)建模的相关知识与方法, 将湖泊水环境中的各种实体, 如政府、排污企业以及各种水生生物等抽象为具有一定智能的主体, 建立湖泊水环境智能决策支持系统. 并将强化学习方法应用到智能决策支持系统中, 实现湖泊水污染的智能预测与预警. 最后, 以太湖流域为应用背景, 进行了初步的仿真实验, 实验结果验证了该方法的有效性.

Reinforcement learning for DSS based on multi-agent model:A case of lake water environment DSS

The lake water environmental problem has been more and more serious.It is a very important subject to find a more effective way of water pollution control.In this paper,the lake water environment decision support system(DSS) is set up,using the knowledge and methods of complex system and multi-agent modeling.The various entities in the lake water environment(such as government,polluting enterprise and a lot of aquatic organisms) are abstracted as the agents,which have some certain intelligence. A method based on reinforcement learning is proposed to achieve the intelligent prediction and warning of the lake water pollution.At last,a preliminary simulation experiment is conducted on the application of Taihu Lake basin.The experiment results show that the proposed method is effective.