Representation Learning

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.

We propose a novel framework to controller design in environments with a two-level structure: a high-level graph in which each vertex is populated by a Markov decision process, called a ``room', with several low-level objectives. We proceed as follows. First, we apply deep reinforcement learning (DRL) to obtain low-level policies for each room and objective. Second, we apply reactive synthesis to obtain a planner that selects which low-level policy to apply in each room. Reactive synthesis refers to constructing a planner for a given model of the environment that satisfies a given objective (typically specified as a temporal logic formula) by design. The main advantage of the framework is formal guarantees. In addition, the framework enables a “separation of concerns”: low-level tasks are addressed using DRL, which enables scaling to large rooms of unknown dynamics, reward engineering is only done locally, and policies can be reused, whereas users can specify high-level tasks intuitively and naturally. The central challenge in synthesis is the need for a model of the rooms. We address this challenge by developing a DRL procedure to train concise “latent” policies together with latent abstract rooms, both paired with PAC guarantees on performance and abstraction quality. Unlike previous approaches, this circumvents a model distillation step. We demonstrate feasibility in a case study involving agent navigation in an environment with moving obstacles

Partially Observable Markov Decision Processes (POMDPs) are useful tools to model environments where the full state cannot be perceived by an agent. As such the agent needs to reason taking into account the past observations and actions. However, simply remembering the full history is generally intractable due to the exponential growth in the history space. Keeping a probability distribution that models the belief over what the true state is can be used as a sufficient statistic of the history, but its computation requires access to the model of the environment and is also intractable. Current state-of-the-art algorithms use Recurrent Neural Networks (RNNs) to compress the observation-action history aiming to learn a sufficient statistic, but they lack guarantees of success and can lead to suboptimal policies. To overcome this, we propose the Wasserstein-Belief-Updater (WBU), an RL algorithm that learns a latent model of the POMDP and an approximation of the belief update. Our approach comes with theoretical guarantees on the quality of our approximation ensuring that our outputted beliefs allow for learning the optimal value function.

In real-world problems, decision makers often have to balance multiple objectives, which can result in trade-offs. One approach to finding a compromise is to use a multi-objective approach, which builds a set of all optimal trade-offs called a Pareto front. Learning the Pareto front requires exploring many different parts of the state- space, which can be time-consuming and increase the chances of encountering undesired or dangerous parts of the state-space. In this preliminary work, we propose a method that combines two frameworks, Pareto Conditioned Networks (PCN) and Wasserstein auto-encoded MDPs (WAE-MDPs), to efficiently learn all possible trade-offs while providing formal guarantees on the learned poli- cies. The proposed method learns the Pareto-optimal policies while providing safety and performance guarantees, especially towards unexpected events, in the multi-objective setting.

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.