Wasserstein Auto-encoded MDPs: Formal Verification of Efficiently Distilled RL Policies with Many-sided Guarantees

Florent Delgrange, Ann Nowe, Guillermo A. Pérez

Onderzoeksoutput: Conference paper

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Although deep reinforcement learning (DRL) has many success stories, the large-scale deployment of policies learned through these advanced techniques in safety-critical scenarios is hindered by their lack of formal guarantees. Variational Markov Decision Processes (VAE-MDPs) are discrete latent space models that provide a reliable framework for distilling formally verifiable controllers from any RL policy. While the related guarantees address relevant practical aspects such as the satisfaction of performance and safety properties, the VAE approach suffers from several learning flaws (posterior collapse, slow learning speed, poor dynamics estimates), primarily due to the absence of abstraction and representation guarantees to support latent optimization. We introduce the Wasserstein auto-encoded MDP (WAE-MDP), a latent space model that fixes those issues by minimizing a penalized form of the optimal transport between the behaviors of the agent executing the original policy and the distilled policy, for which the formal guarantees apply. Our approach yields bisimulation guarantees while learning the distilled policy, allowing concrete optimization of the abstraction and representation model quality. Our experiments show that, besides distilling policies up to 10 times faster, the latent model quality is indeed better in general. Moreover, we present experiments from a simple time-to-failure verification algorithm on the latent space. The fact that our approach enables such simple verification techniques highlights its applicability.
Originele taal-2English
TitelThe Eleventh International Conference on Learning Representations
SubtitelICLR 2023
Plaats van productieKigali, Rwanda
Aantal pagina's30
StatusPublished - 1 feb 2023
EvenementThe Eleventh International Conference on Learning Representations: ICLR 2023 - Kigali Convention Centre, Kigali, Rwanda
Duur: 1 mei 20235 mei 2023
Congresnummer: 11


ConferenceThe Eleventh International Conference on Learning Representations
Verkorte titelICLR 2023
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