Synthesizing Safe Policies under Probabilistic Constraints with Reinforcement Learning and Bayesian Model Checking

• URL: http://arxiv.org/abs/2005.03898v2
• Date: Sat, 6 Feb 2021 10:13:36 GMT
• Title: Synthesizing Safe Policies under Probabilistic Constraints with Reinforcement Learning and Bayesian Model Checking
• Authors: Lenz Belzner and Martin Wirsing
• Abstract summary: We introduce a framework for specification of requirements for reinforcement learners in constrained settings. We show that an agent's confidence in constraint satisfaction provides a useful signal for balancing optimization and safety in the learning process.
• Score: 4.797216015572358
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose to leverage epistemic uncertainty about constraint satisfaction of a reinforcement learner in safety critical domains. We introduce a framework for specification of requirements for reinforcement learners in constrained settings, including confidence about results. We show that an agent's confidence in constraint satisfaction provides a useful signal for balancing optimization and safety in the learning process.

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