Deep Reinforcement Learning (DRL) is an essential subfield of Artificial Intelligence (AI),
where agents interact with environments to learn policies for solving complex tasks. In recent …

Cooperative multi-agent reinforcement learning (c-MARL) is widely applied in safety-critical
scenarios, thus the analysis of robustness for c-MARL models is profoundly important …

Multi-Agent Reinforcement Learning (MARL) has been widely applied in many fields such
as smart traffic and unmanned aerial vehicles. However, most MARL algorithms are …

Abstract Multi-Agent Reinforcement Learning (MARL) trains the decision models of
cooperative agents by making them gain the highest rewards. The Centralized Training with …

Multiagent Reinforcement Learning (MARL) plays a pivotal role in intelligent vehicle
systems, offering solutions for complex decision-making, coordination, and adaptive …

Reinforcement learning (RL) is an actively growing field that is seeing increased usage in
real-world, safety-critical applications--making it paramount to ensure the robustness of RL …