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Safe Reinforcement Learning via a Model-Free Safety Certifier.

Amir Modares, Nasser Sadati, Babak Esmaeili

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    This summary is machine-generated.

    This study introduces a data-driven safe reinforcement learning (RL) algorithm that bypasses system modeling for enhanced safety and stability. It ensures convergence to desired states, avoiding issues with traditional model-based approaches.

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    Area of Science:

    • Control Systems Engineering
    • Machine Learning
    • Nonlinear Dynamics

    Background:

    • Traditional safe reinforcement learning (RL) often relies on system models, which can lead to conservative interventions or convergence to undesired states.
    • Model-based safety certifiers may struggle with performance compromises and ensuring both safety and stability simultaneously.

    Purpose of the Study:

    • To develop a data-driven safe reinforcement learning (RL) algorithm for discrete-time nonlinear systems that bypasses explicit system model identification.
    • To design a safety certifier that ensures system safety and stability without relying on a predefined system model.

    Main Methods:

    • A data-driven safety certifier is proposed, directly learning to intervene in RL agent actions.
    • Linear Parameter Varying (LPV) systems with polytopic disturbances are used to model the nonlinear system.
    • Data-based λ-contractivity conditions and Minkowski functions are employed for safety certification and controller integration.

    Main Results:

    • The proposed method directly learns a robust safety certifier, bypassing system model identification.
    • It resolves conflicts between safety and stability requirements, assuring convergence to the desired equilibrium point.
    • A direct safe learning approach is demonstrated to outperform model-based certifiers in specific scenarios.

    Conclusions:

    • The data-driven approach offers a robust and effective method for safe reinforcement learning in nonlinear systems.
    • This technique enhances system performance by avoiding conservative interventions associated with model-based methods.
    • The developed algorithm ensures both safety and stability while guaranteeing convergence to the intended operational state.