Cover Image

Formally Verifying Deep Reinforcement Learning Controllers with Lyapunov Barrier Certificates

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Mandal, Udayan, Amir, Guy, Wu, Haoze, Daukantas, Ieva, Newell, Fletcher Lee, Ravaioli, Umberto J., Meng, Baoluo, Durling, Michael, Ganai, Milan, Shim, Tobey, Katz, Guy, Barrett, Clark
Format:	Preprint
Published:	2024
Subjects:	Artificial Intelligence Machine Learning Systems and Control
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Safe and Reliable Training of Learning-Based Aerospace Controllers
by: Mandal, Udayan, et al.
Published: (2024)

Formal Synthesis of Certifiably Robust Neural Lyapunov-Barrier Certificates
by: Wang, Chengxiao, et al.
Published: (2026)

On Improving Deep Active Learning with Formal Verification
by: Spiegelman, Jonathan, et al.
Published: (2025)

Proof Minimization in Neural Network Verification
by: Isac, Omri, et al.
Published: (2025)

PICID: Proof-Driven Clause Learning in Neural Network Verification
by: Isac, Omri, et al.
Published: (2025)

Verifying the Generalization of Deep Learning to Out-of-Distribution Domains
by: Amir, Guy, et al.
Published: (2024)

Transforming Natural Language Requirements to Formalism Using LLMs
by: Baoluo Meng, et al.
Published: (2025)

Abstraction-Based Proof Production in Formal Verification of Neural Networks
by: Elboher, Yizhak Yisrael, et al.
Published: (2025)

Self-Supervised Bootstrapping of Action-Predictive Embodied Reasoning
by: Ganai, Milan, et al.
Published: (2026)

Efficiently Computing Compact Formal Explanations
by: Wu, Min, et al.
Published: (2024)

Local vs. Global Interpretability: A Computational Complexity Perspective
by: Bassan, Shahaf, et al.
Published: (2024)

Hard to Explain: On the Computational Hardness of In-Distribution Model Interpretation
by: Amir, Guy, et al.
Published: (2024)

Marabou 2.0: A Versatile Formal Analyzer of Neural Networks
by: Wu, Haoze, et al.
Published: (2024)

Incremental Neural Network Verification via Learned Conflicts
by: Elsaleh, Raya, et al.
Published: (2026)

Unifying Formal Explanations: A Complexity-Theoretic Perspective
by: Bassan, Shahaf, et al.
Published: (2026)

Analyzing Adversarial Inputs in Deep Reinforcement Learning
by: Corsi, Davide, et al.
Published: (2024)

What makes an Ensemble (Un) Interpretable?
by: Bassan, Shahaf, et al.
Published: (2025)

Satisfiability Modulo Theories for Verifying MILP Certificates
by: Wood, Kenan, et al.
Published: (2023)

Formal Mechanistic Interpretability: Automated Circuit Discovery with Provable Guarantees
by: Hadad, Itamar, et al.
Published: (2026)

Lemur: Integrating Large Language Models in Automated Program Verification
by: Wu, Haoze, et al.
Published: (2023)

Cubing for Tuning
by: Wu, Haoze, et al.
Published: (2025)

Shield Synthesis for LTL Modulo Theories
by: Rodriguez, Andoni, et al.
Published: (2024)

The FABRIC Strategy for Verifying Neural Feedback Systems
by: Akinwande, Samuel I., et al.
Published: (2026)

Learning a Formally Verified Control Barrier Function in Stochastic Environment
by: Tayal, Manan, et al.
Published: (2024)

Formally Verified Certification of Unsolvability of Temporal Planning Problems
by: Wang, David, et al.
Published: (2025)

Not All Invariants Are Equal: Curating Training Data to Accelerate Program Verification with SLMs
by: Pinto, Ido, et al.
Published: (2026)

Hamilton-Jacobi Reachability in Reinforcement Learning: A Survey
by: Ganai, Milan, et al.
Published: (2024)

Formally Verified Physics-Informed Neural Control Lyapunov Functions
by: Liu, Jun, et al.
Published: (2024)

A New Strategy for Verifying Reach-Avoid Specifications in Neural Feedback Systems
by: Akinwande, Samuel I., et al.
Published: (2026)

Verified SHAP: Provable Bounds for Exact Shapley Values of Neural Networks
by: Boetius, David, et al.
Published: (2026)

FAME: Formal Abstract Minimal Explanation for Neural Networks
by: Boumazouza, Ryma, et al.
Published: (2026)

Verification-Guided Shielding for Deep Reinforcement Learning
by: Corsi, Davide, et al.
Published: (2024)

Talking with Verifiers: Automatic Specification Generation for Neural Network Verification
by: Elboher, Yizhak Y., et al.
Published: (2026)

Exploring and Evaluating Interplays of BPpy with Deep Reinforcement Learning and Formal Methods
by: Yaacov, Tom, et al.
Published: (2025)

ReSMT: An SMT-Based Tool for Reverse Engineering
by: Somech, Nir, et al.
Published: (2025)

Learning to Split: A Reinforcement-Learning-Guided Splitting Heuristic for Neural Network Verification
by: Swisa, Maya, et al.
Published: (2025)

On Integrating Large Language Models and Scenario-Based Programming for Improving Software Reliability
by: Berzack, Ayelet, et al.
Published: (2025)

Neural Network Verification using Partial Multi-Neuron Relaxation
by: Shmuel, Ido, et al.
Published: (2026)

Formal Safety Guarantees for Autonomous Vehicles using Barrier Certificates
by: Barhoumi, Oumaima, et al.
Published: (2026)

Formally Verified Neural Lyapunov Function for Incremental Input-to-State Stability of Unknown Systems
by: Basu, Ahan, et al.
Published: (2025)