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  1. Home
  3. Scalable And Efficient Deep Reinforcement Learning-based Model Checker For Computation Tree Logic.
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  3. Scalable And Efficient Deep Reinforcement Learning-based Model Checker For Computation Tree Logic.

Related Experiment Video

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
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Scalable and Efficient Deep Reinforcement Learning-Based Model Checker for Computation Tree Logic.

Ghalya Alwhishi, Jamal Bentahar, Amine Andam

    IEEE Transactions on Neural Networks and Learning Systems
    |April 21, 2026

    View abstract on PubMed

    Summary
    This summary is machine-generated.

    This study introduces a deep reinforcement learning (DRL) model checker that verifies computation tree logic (CTL) formulas efficiently. The DRL-CTL approach offers scalable, reliable, and fast verification for complex systems, outperforming traditional methods.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Formal Methods

    Background:

    • Formal verification is crucial for complex systems, but traditional model checking faces scalability issues.
    • Existing learning-based methods lack formal guarantees and interpretability.
    • There's a need for efficient, scalable, and reliable learning-based verification techniques.

    Purpose of the Study:

    • To introduce a novel deep reinforcement learning (DRL)-based model checking framework for verifying computation tree logic (CTL) formulas.
    • To develop a scalable and efficient alternative to traditional symbolic model checking.

    Main Methods:

    • A deep reinforcement learning (DRL) framework using proximal policy optimization (PPO) was developed.
    • The framework interprets CTL semantics on Kripke structures without symbolic state-space traversal.
  • Reward functions were designed for CTL operators, incorporating fixed-point reasoning for global properties.

    • Main Results:

    • The DRL-CTL checker achieved near-constant inference time (approx. 2 ms per formula).
    • Verification time was reduced by up to 90% compared to traditional model checkers.
    • The method scales to models with over 10^1192 states and provides identical verification outcomes.

    Conclusions:

    • Deep reinforcement learning (DRL) presents a scalable, efficient, and explainable alternative for CTL model checking.
    • The proposed framework overcomes the limitations of traditional symbolic methods.
    • DRL-based verification offers significant improvements in computational efficiency and scalability.