40 citations · 70 across the 5 of their papers we have counts for
3 papers · 1 filter
CertRL: Formalizing Convergence Proofs for Value and Policy Iteration in Coq
Koundinya Vajjha, Avraham Shinnar, Vasily Pestun +2
Reinforcement learning algorithms solve sequential decision-making problems in probabilistic environments by optimizing for long-term reward. The desire to use reinforcement learni…
Verifiably Safe Exploration for End-to-End Reinforcement Learning
Nathan Hunt, Nathan Fulton, Sara Magliacane +3
Deploying deep reinforcement learning in safety-critical settings requires developing algorithms that obey hard constraints during exploration. This paper contributes a first appro…
Formal Verification of End-to-End Learning in Cyber-Physical Systems: Progress and Challenges
Nathan Fulton, Nathan Hunt, Nghia Hoang +1
Autonomous systems -- such as self-driving cars, autonomous drones, and automated trains -- must come with strong safety guarantees. Over the past decade, techniques based on forma…