Safe reinforcement learning for probabilistic reachability and safety specifications: A Lyapunov-based approach

24 Feb 2020Subin HuhInsoon Yang

Emerging applications in robotics and autonomous systems, such as autonomous driving and robotic surgery, often involve critical safety constraints that must be satisfied even when information about system models is limited. In this regard, we propose a model-free safety specification method that learns the maximal probability of safe operation by carefully combining probabilistic reachability analysis and safe reinforcement learning (RL)... (read more)

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