Use of Reinforcement Learning in the Modeling of Ring-Type Water Networks

Aleksandar Buchkovski, Zoran Markov, Viktor Iliev, Darko Babunski

Use of Reinforcement Learning in the Modeling of Ring-Type Water Networks

Date Issued

2023-06-06

Author(s)

Aleksandar Buchkovski, Zoran Markov, Viktor Iliev, Darko Babunski

Abstract

In this paper it is proposed to use reinforcement learning
(RL) based predictive control of ring-type water supply network for
finding the optimal path from point of water supply to point of water
consumption avoiding some obstacles that may arise along the way.
An example of an obstruction may be a defect in a section point
(consumer) through which the water cannot flow. Ring-type water
supply network consists of a number of closed rings surrounding the
consumers while supplying them with water through sections. In this
paper, a computer program was developed in LabVIEW in order to
calculate the optimal path from point of water supply to point of water
consumption based on reinforcement learning techniques. In order to
achieve the goal, Markov Decision Processes, Q-Learning, Bellman
Equations and Policy Gradients were used. The results show the
optimal path.

Subjects

Reinforcement learnin...

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Use of Reinforcement Learning in the Modeling of Ring-Type WaterNetworks - MECO 2023.pdf

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