Opposition-based reinforcement learning in the management of water resources

M. Mahootchi, H. R. Tizhoosh, K. Ponnambalam

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Opposition-Based Learning (OBL) is a new scheme in machine intelligence. In this paper, an OBL version Q-learning which exploits opposite quantities to accelerate the learning is used for management of single reservoir operations. In this method, an agent takes an action, receives reward, and updates its knowledge in terms of action-value functions. Fuurthermore, the transition function which is the balance equation in the optimization model determines the next state and updates the actionvalue function pertinent to opposite action. Two type of opposite actions will be defined. It will be demonstrated that using OBL can significantly improve the efficiency of the operating policy within limited iterations. It is also shown that this technique is more robust than Q-Learning.

Original languageEnglish (US)
Title of host publicationProceedings of the 2007 IEEE Symposium on Approximate Dynamic Programming and Reinforcement Learning, ADPRL 2007
Pages217-224
Number of pages8
DOIs
StatePublished - 2007
Event2007 IEEE Symposium on Approximate Dynamic Programming and Reinforcement Learning, ADPRL 2007 - Honolulu, HI, United States
Duration: Apr 1 2007Apr 5 2007

Publication series

NameProceedings of the 2007 IEEE Symposium on Approximate Dynamic Programming and Reinforcement Learning, ADPRL 2007

Conference

Conference2007 IEEE Symposium on Approximate Dynamic Programming and Reinforcement Learning, ADPRL 2007
Country/TerritoryUnited States
CityHonolulu, HI
Period4/1/074/5/07

Keywords

  • Opposite action
  • Q-learning
  • Reinforcement learning
  • Water reservoirs

ASJC Scopus subject areas

  • Computer Science Applications
  • Software

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