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

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-constraint offline reinforcement learning (RL) promises to learn policies that satisfy
both cumulative and state-wise costs from offline datasets. This arrangement provides an …

In this work, we study two-player zero-sum stochastic games and develop a variant of the
smoothed best-response learning dynamics that combines independent learning dynamics …

The large-scale integration of intermittent renewable energy resources introduces increased
uncertainty and volatility to the supply side of power systems, thereby complicating system …

Deployment of Reinforcement Learning (RL) algorithms for robotics applications in the real
world requires ensuring the safety of the robot and its environment. Safe Robot RL (SRRL) is …

Reconfigurable manufacturing system is considered as a promising next-generation
manufacturing paradigm. However, limited equipment and complex product processes add …

The integrated satellite-terrestrial network (ISTN) system has experienced significant growth,
offering seamless communication services in remote areas with limited terrestrial …

Due to a lack of safety considerations, a wide range of multiagent reinforcement learning
(MARL) applications are limited in real-world environments. Thus, ensuring MARL safety is …

Reinforcement Learning (RL) is a potent tool for sequential decision-making and has
achieved performance surpassing human capabilities across many challenging real-world …