3 papers
cs.LO2025
Explanations for Unrealizability of Infinite-State Safety Shields
Andoni Rodriguez, Irfansha Shaik, Davide Corsi +2
Safe Reinforcement Learning focuses on developing optimal policies while ensuring safety. A popular method to address such task is shielding, in which a correct-by-construction saf…
cs.LG2025
Adapting World Models with Latent-State Dynamics Residuals
JB Lanier, Kyungmin Kim, Armin Karamzade +5
Simulation-to-reality reinforcement learning (RL) faces the critical challenge of reconciling discrepancies between simulated and real-world dynamics, which can severely degrade ag…
cs.LG2024
Realizable Continuous-Space Shields for Safe Reinforcement Learning
Kyungmin Kim, Davide Corsi, Andoni Rodriguez +5
While Deep Reinforcement Learning (DRL) has achieved remarkable success across various domains, it remains vulnerable to occasional catastrophic failures without additional safegua…