@article{Delgrange_Nowé_Pérez_2022, title={Distillation of RL Policies with Formal Guarantees via Variational Abstraction of Markov Decision Processes}, volume={36}, url={https://ojs.aaai.org/index.php/AAAI/article/view/20602}, DOI={10.1609/aaai.v36i6.20602}, abstractNote={We consider the challenge of policy simplification and verification in the context of policies learned through reinforcement learning (RL) in continuous environments. In well-behaved settings, RL algorithms have convergence guarantees in the limit. While these guarantees are valuable, they are insufficient for safety-critical applications. Furthermore, they are lost when applying advanced techniques such as deep-RL. To recover guarantees when applying advanced RL algorithms to more complex environments with (i) reachability, (ii) safety-constrained reachability, or (iii) discounted-reward objectives, we build upon the DeepMDP framework to derive new bisimulation bounds between the unknown environment and a learned discrete latent model of it. Our bisimulation bounds enable the application of formal methods for Markov decision processes. Finally, we show how one can use a policy obtained via state-of-the-art RL to efficiently train a variational autoencoder that yields a discrete latent model with provably approximately correct bisimulation guarantees. Additionally, we obtain a distilled version of the policy for the latent model.}, number={6}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Delgrange, Florent and Nowé, Ann and Pérez, Guillermo A.}, year={2022}, month={Jun.}, pages={6497-6505} }