Verifiably Safe Off-Model Reinforcement Learning

1 code implementation • 14 Feb 2019 • Nathan Fulton, Andre Platzer

Through a combination of design-time model updates and runtime model falsification, we provide a first approach toward obtaining formal safety proofs for autonomous systems acting in heterogeneous environments.

reinforcement-learning Reinforcement Learning (RL)