  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.
