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Updated: Jul 9, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
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提高深度强化学习在城市排水系统运行中的可解释性

Wenchong Tian1, Guangtao Fu2, Kunlun Xin3

  • 1College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China; Key Laboratory of Urban Water Supply, Water Saving and Water Environment Governance in the Yangtze River Delta of Ministry of Water Resources, Shanghai 200092, PR China.

Water research
|December 2, 2023
PubMed
概括

本研究引入了一个框架,以提高对深度强化学习 (DRL) 对于城市排水系统 (UDS) 控制的理解. 这些方法提高了可解释性,促进了对实时控制的DRL应用程序的信任.

关键词:
深度强化学习的学习.可以解释性 解释性索博尔灵敏度分析基于树的代孕模型.城市排水系统城市排水系统

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