Cyber Physical Systems (CPS) are characterized by their ability to integrate the physical and
information or cyber worlds. Their deployment in critical infrastructure have demonstrated a …

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 …

In real-world multi-agent reinforcement learning (MARL) applications, agents may not have
perfect state information (eg, due to inaccurate measurement or malicious attacks), which …

A trustworthy reinforcement learning algorithm should be competent in solving challenging
real-world problems, including {robustly} handling uncertainties, satisfying {safety} …

There has been an increasing interest in using neural networks in closed-loop control
systems to improve performance and reduce computational costs for on-line implementation …

Neural Networks (NNs) can provide major empirical performance improvements for closed-
loop systems, but they also introduce challenges in formally analyzing those systems' safety …

Various methods for Multi-Agent Reinforcement Learning (MARL) have been developed
with the assumption that agents' policies are based on accurate state information. However …

As reinforcement learning (RL) has achieved great success and been even adopted in
safety-critical domains such as autonomous vehicles, a range of empirical studies have …

Despite considerable advances in deep reinforcement learning, it has been shown to be
highly vulnerable to adversarial perturbations to state observations. Recent efforts that have …

Despite the substantial advancements in reinforcement learning (RL) in recent years,
ensuring trustworthiness remains a formidable challenge when applying this technology to …