The robustness of deep neural networks has received significant interest recently, especially
when being deployed in safety-critical systems, as it is important to analyze how sensitive …

Neural networks (NNs) playing the role of controllers have demonstrated impressive
empirical performance on challenging control problems. However, the potential adoption of …

The rapid advancement of edge and cloud computing platforms, vehicular ad-hoc networks,
and machine learning techniques have brought both opportunities and challenges for next …

Safe exploration is essential for the practical use of reinforcement learning (RL) in many real-
world scenarios. In this paper, we present a generalized safe exploration (GSE) problem as …

We investigate a novel safe reinforcement learning problem with step-wise violation
constraints. Our problem differs from existing works in that we focus on stricter step-wise …

Model-based reinforcement learning has been widely studied for controller synthesis in
cyber-physical systems (CPSs). In particular, for safety-critical CPSs, it is important to …

Reinforcement Learning (RL) in the context of safe exploration has long grappled with the
challenges of the delicate balance between maximizing rewards and minimizing safety …

Ensuring safety is critical when applying reinforcement learning (RL) to real-world problems.
Consequently, safe RL emerges as a fundamental and powerful paradigm for safely …

Ensuring safety is important for the practical deployment of reinforcement learning (RL).
Various challenges must be addressed, such as handling stochasticity in the environments …

Recent advances in constrained reinforcement learning (RL) have endowed reinforcement
learning with certain safety guarantees. However, deploying existing constrained RL …