Competitive Function Approximation for Reinforcement Learning IRI Technical Report

  • Alejandro Agostini Enric Celaya
  • Published 2014

Abstract

The application of reinforcement learning to problems with continuous domains requires representing the value function by means of function approximation. We identify two aspects of reinforcement learning that make the function approximation process hard: non-stationarity of the target function and biased sampling. Non-stationarity is the result of the… (More)

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