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 …

Multi-agent reinforcement learning (MARL) has exploded in popularity in recent years. Many
approaches have been developed but they can be divided into three main types: centralized …

The importance of the decentralized training with decentralized execution (DTDE)
framework is well-known in the study of multiagent reinforcement learning. In many real …

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 …

In multi-agent cooperative tasks, the presence of heterogeneous agents is familiar.
Compared to cooperation among homogeneous agents, collaboration requires considering …

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

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 …

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 …

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 …