Decentralized learning has shown great promise for cooperative multi-agent reinforcement
learning (MARL). However, non-stationarity remains a significant challenge in fully …

Exploration in decentralized cooperative multi-agent reinforcement learning faces two
challenges. One is that the novelty of global states is unavailable, while the novelty of local …

Metaverse is expected to provide mobile users with emerging applications both in regular
situation like intelligent transportation services and in emergencies like wireless search and …

Fully decentralized learning, where the global information, ie, the actions of other agents, is
inaccessible, is a fundamental challenge in cooperative multi-agent reinforcement learning …

While decentralized training is attractive in multi-agent reinforcement learning (MARL) for its
excellent scalability and robustness, its inherent coordination challenges in collaborative …

Cooperative multi-agent reinforcement learning is a powerful tool to solve many real-world
cooperative tasks, but restrictions of real-world applications may require training the agents …

Offline reinforcement learning could learn effective policies from a fixed dataset, which is
promising for real-world applications. However, in offline decentralized multi-agent …

There is a prevalence of multiagent reinforcement learning (MARL) methods that engage in
centralized training. These methods rely on all the agents sharing various types of …

Cooperative multi-agent reinforcement learning (MARL) is a wellabstracted model for a
broad range of real applications, including logistics [10], traffic signal control [33], power …

Effective multi-agent collaboration is imperative for solving complex, distributed problems. In
this context, two key challenges must be addressed: first, autonomously identifying optimal …