Compositional Policy Learning in Stochastic Control Systems with Formal Guarantees Institute of Science and Technology Austria (ISTA) Massachusetts Institute of Technology Klosterneuburg, Austria

Reinforcement learning has shown promising results in learning neural network policies for complicated control tasks. However, the lack of formal guarantees about the behavior of such policies remains an impediment to their deployment. We propose a novel method for learning a composition of neural network policies in stochastic environments, along with a formal certificate which guarantees that a specification over the policy's behavior is satisfied with the desired probability.