Real-world multi-agent tasks usually involve dynamic team composition with the emergence
of roles, which should also be a key to efficient cooperation in multi-agent reinforcement …

Many real-world multi-agent tasks exhibit a nearly decomposable structure, where
interactions among agents within the same interaction set are strong while interactions …

With the popularization of intelligence, the necessity of cooperation between intelligent
machines makes the research of collaborative multi-agent reinforcement learning (MARL) …

Deep Reinforcement Learning has shown some promise in dynamic traffic signal control by
adapting to real-time traffic conditions. However, multi-intersection control presents …

In recent years, cooperative multiagent deep reinforcement learning (MADRL) has received
increasing research interest and has been widely applied to computer games and …

In the context of humans operating with artificial or autonomous agents in a hybrid team, it is
essential to accurately identify when to authorize those team members to perform actions …

Parameter sharing is a common strategy in multiagent reinforcement learning (MARL) to
make the training more efficient and scalable. However, applying parameter sharing among …