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S. Curi; A. Lederer; S. Hirche; A. Krause
Safe Reinforcement Learning via Confidence-Based Filters
Ensuring safety is a crucial challenge when deploying reinforcement learning (RL) to real-world systems. We develop confidence-based safety filters, a control-theoretic approach for certifying state safety constraints for nominal policies learnt via standard RL techniques, based on probabilistic dynamics models. Our approach is based on a reformulation of state constraints in terms of cost functions, reducing safety verification to a standard RL task. By exploiting the concept of hallucinating i...     »
data_driven_control; coman
Book / Congress title:
Proceedings of the IEEE Conference on Decision and Control
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