A new optimal stepsize for approximate dynamic programming

Ilya O. Ryzhov, Peter I. Frazier, Warren B. Powell

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


Approximate dynamic programming (ADP) has proven itself in a wide range of applications spanning large-scale transportation problems, health care, revenue management, and energy systems. The design of effective ADP algorithms has many dimensions, but one crucial factor is the stepsize rule used to update a value function approximation. Many operations research applications are computationally intensive, and it is important to obtain good results quickly. Furthermore, the most popular stepsize formulas use tunable parameters and can produce very poor results if tuned improperly. We derive a new stepsize rule that optimizes the prediction error in order to improve the short-term performance of an ADP algorithm. With only one, relatively insensitive tunable parameter, the new rule adapts to the level of noise in the problem and produces faster convergence in numerical experiments.

Original languageEnglish (US)
Article number6897935
Pages (from-to)743-758
Number of pages16
JournalIEEE Transactions on Automatic Control
Issue number3
StatePublished - Mar 1 2015

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering


  • Approximate dynamic programming (ADP)
  • Kalman filter
  • simulation-based optimization
  • stochastic approximation


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