Recently an influx of studies claims emergent cognitive abilities in large language models
(LLMs). Yet, most rely on anecdotes, overlook contamination of training sets, or lack …

Reinforcement learning (RL) methods usually treat reward functions as black boxes. As
such, these methods must extensively interact with the environment in order to discover …

In recent years, formal methods have been extensively used in the design of autonomous
systems. By employing mathematically rigorous techniques, formal methods can provide …

We study the problem of learning control policies for complex tasks given by logical
specifications. Recent approaches automatically generate a reward function from a given …

This paper concerns the efficient construction of a safety shield for reinforcement learning.
We specifically target scenarios that incorporate uncertainty and use Markov decision …

There have been tremendous developments in theories and technologies in control for
smart systems. In this paper we review applications to various systems that are crucial for the …

This letter investigates the motion planning of autonomous dynamical systems modeled by
Markov decision processes (MDP) with unknown transition probabilities over continuous …

This paper presents the concept of an adaptive safe padding that forces Reinforcement
Learning (RL) to synthesise optimal control policies while ensuring safety during the …

The combination of data-driven learning methods with formal reasoning has seen a surge of
interest, as either area has the potential to bolstering the other. For instance, formal methods …

Reinforcement learning has shown promising results in learning neural network policies for
complicated control tasks. However, the lack of formal guarantees about the behavior of …