Artificial Intelligence

Activating Formal Verification of Deep Reinforcement Learning Policies by Model Checking Bisimilar Latent Space Models
Intelligent agents are computational entities that autonomously interact with an environment to achieve their design objectives. On the one hand, reinforcement learning (RL) encompasses machine learning techniques that allow agents to learn by trial and error a control policy, prescribing how to behave in the environment. Although RL is proven to converge to an optimal policy under some assumptions, the guarantees vanish with the introduction of advanced techniques, such as deep RL, to deal with high-dimensional state and action spaces. This prevents them from being widely adopted in real-world safety-critical scenarios. On the other hand, formal methods are mathematical techniques that provide guarantees about the correctness of systems. In particular, model checking allows formally verifying the agent’s behaviors in the environment. However, this typically relies on a formal description of the interaction, as well as conducting an exhaustive exploration of the state space. This poses significant challenges because the environment is seldom explicitly accessible. Even when it is, model checking suffers from the curse of dimensionality and struggles to scale to high-dimensional state and action spaces, which are common in deep RL. In this thesis, we leverage the strengths of deep RL to handle realistic scenarios while integrating formal methods to provide guarantees on the agent’s behaviors. Specifically, we activate formal verification of deep RL policies by learning a latent model of the environment, over which we distill the deep RL policy. The outcome is amenable for model checking and is endowed with bisimulation guarantees, which allows to lift the verification results to the original environment. Beyond distillation, we show that our method is also useful for learning representation in the context of deep RL, facilitating the learning of the policy in complex environments. In particular, we present a framework for partially observable environments. We finally show how our method can be leveraged in the context of synthesis, i.e., the automatic generation of controllers from logical specifications with formal guarantees. Precisely, we present how deep RL components learned via our latent space models facilitate synthesis in typically intractable environments.
Activating Formal Verification of Deep Reinforcement Learning Policies by Model Checking Bisimilar Latent Space Models
Wasserstein Auto-encoded MDPs: Formal Verification of Efficiently Distilled RL Policies with Many-sided Guarantees
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.
Wasserstein Auto-encoded MDPs: Formal Verification of Efficiently Distilled RL Policies with Many-sided Guarantees
A Framework for Flexibly Guiding Learning Agents