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Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
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    We introduce an event-driven direct heuristic dynamic programming (dHDP) method to improve reinforcement learning. This approach prevents unnecessary updates from noise, ensuring more stable control policy learning.

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    Area of Science:

    • Machine Learning
    • Control Theory
    • Artificial Intelligence

    Background:

    • Time-driven learning continuously updates models with new data.
    • Direct heuristic dynamic programming (dHDP) is effective for complex control problems.
    • Continuous updates in dHDP can be triggered by insignificant events like noise.

    Purpose of the Study:

    • To develop an event-driven dHDP algorithm to mitigate noise-induced updates.
    • To ensure stability and efficiency in reinforcement learning control systems.
    • To improve the performance of approximate dynamic programming.

    Main Methods:

    • Proposed an event-driven dHDP algorithm.
    • Utilized a Lyapunov function candidate for stability analysis.
    • Proved uniformly ultimately boundedness (UUB) for system states and network weights.

    Main Results:

    • Demonstrated that the event-driven dHDP prevents updates from insignificant events.
    • Proved the UUB of system states and network parameters.
    • Showed convergence of approximate control and cost-to-go functions to Bellman optimality within a finite bound.

    Conclusions:

    • The event-driven dHDP offers a more robust alternative to time-driven dHDP.
    • The proposed method enhances the stability and efficiency of reinforcement learning control.
    • Theoretical guarantees for system boundedness and convergence are established.