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

Multi-agent reinforcement learning (MARL) is a widely used Artificial Intelligence (AI)
technique. However, current studies and applications need to address its scalability, non …

On-ramp merging is a challenging task for autonomous vehicles (AVs), especially in mixed
traffic where AVs coexist with human-driven vehicles (HDVs). In this paper, we formulate the …

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 …

While many multi-agent deep reinforcement learning (MADRL) algorithms have been
implemented for active voltage control (AVC) in power distribution systems, the safety of …

We study the problem of learning controllers for discrete-time non-linear stochastic
dynamical systems with formal reach-avoid guarantees. This work presents the first method …

Connected and automated vehicles (CAVs) require multiple tasks in their seamless
maneuverings. Some essential tasks that require simultaneous management and actions …

In this survey, we review the recent advances in control design methods for robotic multi-
agent systems (MAS), focusing on learning-based methods with safety considerations. We …

Multi-Agent Reinforcement Learning (MARL) algorithms show amazing performance in
simulation in recent years, but placing MARL in real-world applications may suffer safety …

Cost functions are commonly employed in Safe Deep Reinforcement Learning (DRL).
However, the cost is typically encoded as an indicator function due to the difficulty of …