An intelligent agent for optimal river‐reservoir system management
| Autor: | Jeffrey D. Rieker, John W. Labadie |
|---|---|
| Rok vydání: | 2012 |
| Předmět: |
Engineering
business.industry Distributed computing Multi-agent system Probabilistic logic computer.software_genre Intelligent agent Systems management Reinforcement learning Stochastic optimization Markov decision process business computer Simulation Water Science and Technology Graphical user interface |
| Zdroj: | Water Resources Research. 48 |
| ISSN: | 1944-7973 0043-1397 |
| DOI: | 10.1029/2012wr011958 |
| Popis: | [1] A generalized software package is presented for developing an intelligent agent for stochastic optimization of complex river-reservoir system management and operations. Reinforcement learning is an approach to artificial intelligence for developing a decision-making agent that learns the best operational policies without the need for explicit probabilistic models of hydrologic system behavior. The agent learns these strategies experientially in a Markov decision process through observational interaction with the environment and simulation of the river-reservoir system using well-calibrated models. The graphical user interface for the reinforcement learning process controller includes numerous learning method options and dynamic displays for visualizing the adaptive behavior of the agent. As a case study, the generalized reinforcement learning software is applied to developing an intelligent agent for optimal management of water stored in the Truckee river-reservoir system of California and Nevada for the purpose of streamflow augmentation for water quality enhancement. The intelligent agent successfully learns long-term reservoir operational policies that specifically focus on mitigating water temperature extremes during persistent drought periods that jeopardize the survival of threatened and endangered fish species. |
| Databáze: | OpenAIRE |
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