Safety Verification for Model-based Reinforcement Learning Controllers
[2018 - 2019]
Developed a stand-alone verification tool for model-based reinforcement learning controllers. Reachable set analysis was used to determine whether a trained controller will violate any safety constraints imposed on the state space in the presence of noise.