论文标题
深入的加强学习，以实时优化水分配系统中的泵
Deep Reinforcement Learning for Real-Time Optimization of Pumps in Water Distribution Systems
论文作者
论文摘要
在水分配系统（WDS）中，对泵的实时控制可能是一项不可行的任务，因为找到最佳泵速的计算是资源密集的。即使使用常规优化技术，即使使用智能水网络的功能，也无法降低计算需求。深处的增强学习（DRL）在这里作为两个WDS中的泵的控制器提出。基于决斗深Q网络的代理经过训练，以根据瞬时淋巴压数据维持泵速度。一般优化技术（例如Nelder-Mead方法，差异进化）用作基准。与表现最佳的基线相比，DRL药物达到的总效率高于0.98，而加速度约为2倍。提出的方法的主要贡献是代理可以实时运行泵，因为它仅取决于测量数据。如果将WDS替换为液压模拟，则代理在搜索速度上仍然优于常规技术。
Real-time control of pumps can be an infeasible task in water distribution systems (WDSs) because the calculation to find the optimal pump speeds is resource-intensive. The computational need cannot be lowered even with the capabilities of smart water networks when conventional optimization techniques are used. Deep reinforcement learning (DRL) is presented here as a controller of pumps in two WDSs. An agent based on a dueling deep q-network is trained to maintain the pump speeds based on instantaneous nodal pressure data. General optimization techniques (e.g., Nelder-Mead method, differential evolution) serve as baselines. The total efficiency achieved by the DRL agent compared to the best performing baseline is above 0.98, whereas the speedup is around 2x compared to that. The main contribution of the presented approach is that the agent can run the pumps in real-time because it depends only on measurement data. If the WDS is replaced with a hydraulic simulation, the agent still outperforms conventional techniques in search speed.
