Technical Note: \cal Q-Learning
Machine Learning
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
DQL: A New Updating Strategy for Reinforcement Learning Based on Q-Learning
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
MICAI '00 Proceedings of the Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
Ant Colony Optimization
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Water systems often allow efficient water uses via water reuse and/or recirculation. Defining the network layout connecting water-using processes is a complex problem which involves several criteria to optimize, frequently accomplished using Water Pinch technology, optimizing freshwater flowrates entering the system. In this paper, a multiobjective optimization model considering two criteria is presented: (i)the minimization of freshwater consumption, and (ii) the minimization of the cost of the infrastructure required to build the network that make possible the reduction of freshwater consumption. The optimization model considers water reuse between operations and wastewater treatment as the main optimization mechanism. The operation of the Cuernavaca city water distribution system was analyzed under two different operation strategies considering: leak reduction, operation of wastewater treatment plants as they currently operate, operation of wastewater treatment plants at design capacity, and construction of new infrastructure to treat 100 % of discharged wastewater. Results were obtained with MDQL a multiobjective optimization algorithm based on a distributed reinforcement learning framework, and they were validated with mathematical programming.