Reinforcement Learning (RL) has achieved tremendous success in many complex decision-
making tasks. However, safety concerns are raised during deploying RL in real-world …

Abstract Context Machine Learning (ML) has been at the heart of many innovations over the
past years. However, including it in so-called “safety-critical” systems such as automotive or …

This paper presents the Neural Network Verification (NNV) software tool, a set-based
verification framework for deep neural networks (DNNs) and learning-enabled cyber …

Reinforcement learning (RL) has shown excellent performance in the sequential decision-
making problem, where safety in the form of state constraints is of great significance in the …

We present a model-free reinforcement learning algorithm to synthesize control policies that
maximize the probability of satisfying high-level control objectives given as Linear Temporal …

We present REVEL, a partially neural reinforcement learning (RL) framework for provably
safe exploration in continuous state and action spaces. A key challenge for provably safe …

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 …

This paper targets the efficient construction of a safety shield for decision making in
scenarios that incorporate uncertainty. Markov decision processes (MDPs) are prominent …

Machine learning approaches often lack safety guarantees, which are often a key
requirement in real-world tasks. This paper addresses the lack of safety guarantees by …

While reinforcement learning (RL) is gaining popularity in energy systems control, its real-
world applications are limited due to the fact that the actions from learned policies may not …