Cover Image

Verification-Guided Shielding for Deep Reinforcement Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Corsi, Davide, Amir, Guy, Rodriguez, Andoni, Sanchez, Cesar, Katz, Guy, Fox, Roy
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

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

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

Realizable Continuous-Space Shields for Safe Reinforcement Learning
by: Kim, Kyungmin, et al.
Published: (2024)

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

Efficient Dynamic Shielding for Parametric Safety Specifications
by: Corsi, Davide, et al.
Published: (2025)

Explanations for Unrealizability of Infinite-State Safety Shields
by: Rodriguez, Andoni, et al.
Published: (2025)

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

Model-Based Reinforcement Learning under Random Observation Delays
by: Karamzade, Armin, et al.
Published: (2025)

Aquatic Navigation: A Challenging Benchmark for Deep Reinforcement Learning
by: Corsi, Davide, et al.
Published: (2024)

Statistical Runtime Verification for LLMs via Robustness Estimation
by: Levy, Natan, et al.
Published: (2025)

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)

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

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

Formally Verifying Deep Reinforcement Learning Controllers with Lyapunov Barrier Certificates
by: Mandal, Udayan, et al.
Published: (2024)

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

Towards a Certified Proof Checker for Deep Neural Network Verification
by: Desmartin, Remi, et al.
Published: (2023)

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

Knowledge Transfer in Deep Reinforcement Learning via an RL-Specific GAN-Based Correspondence Function
by: Ruman, Marko, et al.
Published: (2022)

DEM: A Method for Certifying Deep Neural Network Classifier Outputs in Aerospace
by: Katz, Guy, et al.
Published: (2024)

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

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

Shielded Deep Reinforcement Learning for Complex Spacecraft Tasking
by: Reed, Robert, et al.
Published: (2024)

Exploration in Knowledge Transfer Utilizing Reinforcement Learning
by: Jedlička, Adam, et al.
Published: (2024)

Representation-Driven Reinforcement Learning
by: Nabati, Ofir, et al.
Published: (2023)

Benchmarks for Reinforcement Learning with Biased Offline Data and Imperfect Simulators
by: Linial, Ori, et al.
Published: (2024)

Additive Models Explained: A Computational Complexity Approach
by: Bassan, Shahaf, et al.
Published: (2025)

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

Bridging Efficiency and Safety: Formal Verification of Neural Networks with Early Exits
by: Elboher, Yizhak Yisrael, et al.
Published: (2025)

Collapsed Inference for Bayesian Deep Learning
by: Zeng, Zhe, et al.
Published: (2023)

Contextual Pre-planning on Reward Machine Abstractions for Enhanced Transfer in Deep Reinforcement Learning
by: Azran, Guy, et al.
Published: (2023)

Optimizing Metachronal Paddling with Reinforcement Learning at Low Reynolds Number
by: Bailey, Alana A., et al.
Published: (2025)

Effective Reward Specification in Deep Reinforcement Learning
by: Roy, Julien
Published: (2024)

Robust Learning with Optimal Error
by: Blanc, Guy
Published: (2026)

Beyond the Surface: Uncovering Implicit Locations with LLMs for Personalized Local News
by: Katz, Gali, et al.
Published: (2025)

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

SHAP Meets Tensor Networks: Provably Tractable Explanations with Parallelism
by: Marzouk, Reda, et al.
Published: (2025)

Random Forest Autoencoders for Guided Representation Learning
by: Aumon, Adrien, et al.
Published: (2025)

Reinforcement Learning from Delayed Observations via World Models
by: Karamzade, Armin, et al.
Published: (2024)

Probabilistic Shielding for Safe Reinforcement Learning
by: Court, Edwin Hamel-De le, et al.
Published: (2025)