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    A new discounted iterative adaptive dynamic programming framework accelerates learning convergence and reduces computational costs for adaptive critic designs. This method enhances value function approximation and policy improvement for dynamic systems.

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    Area of Science:

    • Control Systems Engineering
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Traditional iterative adaptive dynamic programming methods face challenges with convergence speed and computational efficiency.
    • Adaptive critic designs are crucial for solving complex control problems in real-time applications.

    Purpose of the Study:

    • To develop a novel discounted iterative adaptive dynamic programming framework with an adjustable convergence rate.
    • To investigate the convergence properties and closed-loop system stability of the new discounted value iteration (VI) scheme.
    • To present an accelerated learning algorithm with convergence guarantees for adaptive dynamic programming.

    Main Methods:

    • A discounted iterative adaptive dynamic programming framework inspired by the successive relaxation method.
    • Investigation of value function sequence convergence properties and closed-loop system stability.
    • Development of an accelerated learning algorithm incorporating value function approximation and policy improvement.

    Main Results:

    • The new discounted VI scheme demonstrates an adjustable convergence rate for the value function sequence.
    • The developed accelerated learning algorithm guarantees convergence.
    • The proposed methods significantly accelerate convergence and reduce computational cost compared to traditional VI.

    Conclusions:

    • The novel discounted iterative adaptive dynamic programming framework offers enhanced performance in terms of speed and efficiency.
    • The accelerated learning algorithm provides a robust solution for adaptive dynamic programming problems.
    • The approach is validated on a nonlinear ball-and-beam balancing plant, showing superior results.