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A Parallel Framework of Adaptive Dynamic Programming Algorithm With Off-Policy Learning.

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    This study introduces a model-free adaptive dynamic programming (ADP) method for optimal control of nonaffine nonlinear systems. The approach enhances data collection and exploration using parallel agents, ensuring system stability and convergence to the Hamilton-Jacobi-Bellman equation solution.

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    Area of Science:

    • Control Theory
    • Machine Learning
    • Nonlinear Systems

    Background:

    • Optimal control of nonaffine nonlinear systems presents significant challenges.
    • Existing model-free methods often struggle with data efficiency and exploration limitations.

    Purpose of the Study:

    • To develop a novel model-free online adaptive dynamic programming (ADP) approach for optimal control.
    • To enhance data collection and exploration capabilities for improved learning.
    • To guarantee system stability and convergence for the proposed control laws.

    Main Methods:

    • Utilized an off-policy learning mechanism combined with a parallel paradigm employing multithread agents.
    • Implemented an actor-critic (AC) structure with two neural networks (NNs) for Q-function and policy approximation.
    • Employed a policy gradient method for a single-step policy improvement after policy evaluation.

    Main Results:

    • Significantly augmented sampled data through parallel agent interaction and diverse initial states.
    • Demonstrated guaranteed system stability under iterative control laws.
    • Provided convergence analysis proving the Q-function's monotonic non-increasing convergence to the Hamilton-Jacobi-Bellman (HJB) equation solution.
    • Verified the algorithm's effectiveness through two numerical examples.

    Conclusions:

    • The proposed model-free online ADP approach effectively solves optimal control problems for nonaffine nonlinear systems.
    • The parallel learning and exploration strategy enhances data efficiency and robustness.
    • The actor-critic implementation with neural networks provides a practical framework for the developed algorithm.