Formally Verified Approximate Policy Iteration - Schäffeler, Maximilian; Abdulaziz, Mohammad (Proceedings of the AAAI Conference on Artificial Intelligence, 2025)
We present a methodology based on interactive theorem proving that facilitates the development of verified implementations of algorithms for solving factored Markov Decision Processes. As a case study, we formally verify an algorithm for approximate policy iteration in the proof assistant Isabelle/HOL. We show how the verified algorithm can be refined to an executable, verified implementation. Our evaluation on benchmark problems shows that it is practical. As part of the development, we build verified software to certify linear programming solutions. We discuss the verification process and the modifications we made to the algorithm during formalization.