40 citations · 41 across the 3 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…
Verifiably Safe Off-Model Reinforcement Learning
Nathan Fulton, Andre Platzer
The desire to use reinforcement learning in safety-critical settings has inspired a recent interest in formal methods for learning algorithms. Existing formal methods for learning…