Many real-world applications can be described as large-scale games of imperfect
information, which require extensive prior domain knowledge, especially in competitive or …

Multi-access edge computing (MEC) is a novel computing paradigm that leverages nearby
MEC servers to augment the computational capabilities of users with limited computational …

Unmanned aerial vehicle (UAV) confrontation scenarios play a crucial role in the study of
agent behavior selection and decision planning. Multi-agent reinforcement learning (MARL) …

In the UAV swarm, the degradation in the health status of some UAVs often brings negative
effects to the system. To compensate for the negative effect, we present a fault-tolerant Multi …

Multi-agent reinforcement learning (MARL) aims to study the behavior of multiple agents in a
shared environment. Existing communication-based MARL methods seldom consider the …

With the increasing demand for spectrum efficiency and energy efficiency, reconfigurable
intelligent surfaces (RISs) have attracted massive attention due to its low-cost and capability …

Reinforcement-learning (RL) algorithms have made great achievements in many scenarios.
However, in large-scale traffic signal control (TSC) scenarios, RL still falls into local optima …

Various studies have been conducted on Multi-Agent Reinforcement Learning (MARL) to
control multiple agents to drive effectively and safely in a simulation, demonstrating the …

Leveraging global state information to enhance policy optimization is a common approach in
multi-agent reinforcement learning (MARL). Even with the supplement of state information …