Nonlinear reinforcement schemes for learning automata

Humberto E. Garcia, Asok Ray

    Research output: Contribution to journalArticle

    3 Citations (Scopus)

    Abstract

    The development and evaluation of two novel nonlinear reinforcement schemes for learning automata are presented. These schemes are designed to increase the rate of adaptation of the existing LR-P schemes while interacting with nonstationary environments. The first of these two schemes is called a nonlinear scheme incorporating history (NSIH) and the second a nonlinear scheme with unstable zones (NSWUZ). The prime objective of these algorithms is to reduce the number of iterations needed for the action probability vector to reach the desired level of accuracy rather than converge to a specific unit vector in the Cartesian coordinate. Simulation experiments have been conducted to assess the learning properties of NSIH and NSWUZ in nonstationary environments. The simulation results show that the proposed nonlinear algorithms respond to environmental changes faster than the LR-P scheme.

    Original languageEnglish (US)
    Pages (from-to)2204-2207
    Number of pages4
    JournalProceedings of the IEEE Conference on Decision and Control
    Volume4
    StatePublished - 1990

    Fingerprint

    Learning Automata
    Reinforcement
    Experiments
    Unstable
    Unit vector
    Cartesian
    Simulation Experiment
    Converge
    Iteration

    All Science Journal Classification (ASJC) codes

    • Chemical Health and Safety
    • Control and Systems Engineering
    • Safety, Risk, Reliability and Quality

    Cite this

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    title = "Nonlinear reinforcement schemes for learning automata",
    abstract = "The development and evaluation of two novel nonlinear reinforcement schemes for learning automata are presented. These schemes are designed to increase the rate of adaptation of the existing LR-P schemes while interacting with nonstationary environments. The first of these two schemes is called a nonlinear scheme incorporating history (NSIH) and the second a nonlinear scheme with unstable zones (NSWUZ). The prime objective of these algorithms is to reduce the number of iterations needed for the action probability vector to reach the desired level of accuracy rather than converge to a specific unit vector in the Cartesian coordinate. Simulation experiments have been conducted to assess the learning properties of NSIH and NSWUZ in nonstationary environments. The simulation results show that the proposed nonlinear algorithms respond to environmental changes faster than the LR-P scheme.",
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    Nonlinear reinforcement schemes for learning automata. / Garcia, Humberto E.; Ray, Asok.

    In: Proceedings of the IEEE Conference on Decision and Control, Vol. 4, 1990, p. 2204-2207.

    Research output: Contribution to journalArticle

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