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Safe Exploration in Model-based Reinforcement Learning using Control Barrier Functions
[article]
2022
arXiv
pre-print
This paper develops a model-based reinforcement learning (MBRL) framework for learning online the value function of an infinite-horizon optimal control problem while obeying safety constraints expressed as control barrier functions (CBFs). Our approach is facilitated by the development of a novel class of CBFs, termed Lyapunov-like CBFs (LCBFs), that retain the beneficial properties of CBFs for developing minimally-invasive safe control policies while also possessing desirable Lyapunov-like
arXiv:2104.08171v4
fatcat:5gqatbn7bregtkdz6al7ydukpu