Synthesizing safe policies under probabilistic constraints with reinforcement learning and Bayesian model checking

We propose to leverage epistemic uncertainty about constraint satisfaction of a reinforcement learner in safety critical domains. introduce framework for specification requirements learners constrained settings, including confidence results. show that an agent's provides useful signal balancing optimization and the learning process.