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arXiv · 2207.01337

Safe Reinforcement Learning via Confidence-Based Filters

Sebastian Curi·Armin Lederer·Sandra Hirche·Andreas Krause
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Abstract

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 learned 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 inputs, we extend this formulation to determine a "backup" policy that is safe for the unknown system with high probability. Finally, the nominal policy is minimally adjusted at every time step during a roll-out towards the backup policy, such that safe recovery can be guaranteed afterwards. We provide formal safety guarantees, and empirically demonstrate the effectiveness of our approach.

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BibTeXRIS

Sebastian Curi, Armin Lederer, Sandra Hirche, Andreas Krause. 2022-07-04. Safe Reinforcement Learning via Confidence-Based Filters. https://arxiv.org/abs/2207.01337

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