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    Enhanced Exploration Least-Squares Methods for Optimal Stopping Problems

    Forootani, Ali and Tipaldi, Massimo and Iervolino, Raffaele and Dey, Subhrakanti (2022) Enhanced Exploration Least-Squares Methods for Optimal Stopping Problems. IEEE Control Systems Letters, 6. pp. 271-276. ISSN 2475-1456

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    This letter presents an Approximate Dynamic Programming (ADP) least-squares based approach for solving optimal stopping problems with a large state space. By extending some previous work in the area of optimal stopping problems, it provides a framework for their formulation and resolution. The proposed method uses a combined on/off policy exploration mechanism, where states are generated by means of state transition probability distributions different from the ones dictated by the underlying Markov decision processes. The contraction mapping property of the associated projected Bellman operator is analysed as well as the convergence of the resulting algorithm.

    Item Type: Article
    Keywords: Markov processes; Cost function; Probability distribution; Steady-state; Monte Carlo methods; Mathematical model; Computational modeling;
    Academic Unit: Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 18494
    Identification Number:
    Depositing User: Subhrakanti Dey
    Date Deposited: 09 May 2024 11:00
    Journal or Publication Title: IEEE Control Systems Letters
    Publisher: IEEE
    Refereed: Yes
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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