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

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. • Reinforcement learning enables agents to automatically learn safe policies. • Specify systems by a Markov decision process and goal-oriented requirements. • Synthesize safe policy satisfying probabilistic constraints with required confidence. • Solve constrained optimization by an evolutionary strategy and Bayesian verification. • Bayesian model checking computes constraint satisfaction probability and confidence.