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A. Almudevar and E. F. Arruda, “Optimal Approximation Schedules for a Class of Iterative Algorithms, with an Application to Multigrid Value Iteration,” IEEE Transactions on Automatic Control, Vol. 27, No. 12, 2012, pp. 3132-3146. http://dx.doi.org/10.1109/TAC.2012.2203053

has been cited by the following article:

• TITLE: Adaptive Strategies for Accelerating the Convergence of Average Cost Markov Decision Processes Using a Moving Average Digital Filter

  AUTHORS: Edilson F. Arruda, Fabrício Ourique

  KEYWORDS: Average Cost; Markov Decision Processes; Value Iteration; Computational Effort; Gradient

  JOURNAL NAME: American Journal of Operations Research, Vol.3 No.6, October 31, 2013

  ABSTRACT: This paper proposes a technique to accelerate the convergence of the value iteration algorithm applied to discrete average cost Markov decision processes. An adaptive partial information value iteration algorithm is proposed that updates an increasingly accurate approximate version of the original problem with a view to saving computations at the early iterations, when one is typically far from the optimal solution. The proposed algorithm is compared to classical value iteration for a broad set of adaptive parameters and the results suggest that significant computational savings can be obtained, while also ensuring a robust performance with respect to the parameters.