Comparing policy gradient and value function based reinforcement learning methods in simulated electrical power trade

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In electrical power engineering, reinforcement learning algorithms can be used to model the strategies of electricity market participants. However, traditional value function based reinforcement learning algorithms suffer from convergence issues when used with value function approximators. Function approximation is required in this domain to capture the characteristics of the complex and continuous multivariate problem space. The contribution of this paper is the comparison of policy gradient reinforcement learning methods, using artificial neural networks for policy function approximation, with traditional value function based methods in simulations of electricity trade. The methods are compared using an AC optimal power flow based power exchange auction market model and a reference electric power system model.
Original languageEnglish
Pages (from-to)373-380
Number of pages8
JournalIEEE Transactions on Power Systems
Issue number1
Early online date6 Oct 2011
Publication statusPublished - Feb 2012



  • artificial Intelligence
  • game theory
  • gradient methods
  • learning control systems
  • neural network applications
  • power system economics

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