Unmanned aerial vehicle (UAV)-assisted communication is a significant technology in 6G
communication. In order to cope with the dynamic trajectory optimization problem of the air …

In the downlink communication, it is currently challenging for ground users to cope with the
uncertain interference from aerial intelligent jammers. The cooperation and competition …

While multi-agent trust region algorithms have achieved great success empirically in solving
coordination tasks, most of them, however, suffer from a non-stationarity problem since …

In many multi-agent and high-dimensional robotic tasks, the controller can be designed in
either a centralized or decentralized way. Correspondingly, it is possible to use either single …

Multi-agent football poses an unsolved challenge in AI research. Existing work has focused
on tackling simplified scenarios of the game, or else leveraging expert demonstrations. In …

Decentralized cooperative multi-agent deep reinforcement learning (MARL) can be a
versatile learning framework, particularly in scenarios where centralized training is either not …

Decentralized multi-agent cooperative learning is a practical task due to the partially
observed setting both in training and execution. Every agent learns to cooperate without …

The study of decentralized learning or independent learning in cooperative multi-agent
reinforcement learning has a history of decades. Recently empirical studies show that …

\textit {Relative overgeneralization}(RO) occurs in cooperative multi-agent learning tasks
when agents converge towards a suboptimal joint policy due to overfitting to suboptimal …

Single-agent Deep Reinforcement Learning (DRL) is a popular control technique where the
policy controlling agent learns to choose actions that maximize a discounted long-term …