Due to the characteristics of the small size and low cost of unmanned aerial vehicles (UAVs),
Multi-UAV confrontation will play an important role in future wars. The Multi-UAV …

Communication plays a crucial role in information sharing within the field of multi-agent
reinforcement learning (MARL). However, how to transmit information that meets individual …

In recent years, the multiple traveling salesmen problem (MTSP or multiple TSP) has
received increasing research interest and one of its main applications is coordinated …

In the multiple unmanned aerial vehicle (UAV)-assisted downlink communication, it is
challenging for UAV base stations (UAV BSs) to realize trajectory design and resource …

Communication-based multiagent reinforcement learning (MARL) has shown promising
results in promoting cooperation by enabling agents to exchange information. However, the …

We extend trust region policy optimization (TRPO) to cooperative multiagent reinforcement
learning (MARL) for partially observable Markov games (POMGs). We show that the policy …

It is often challenging to design an optimal tracking controller for the continuous stirred tank
reactor (CSTR) system due to its nonlinear nature and physical limitations. This article …

This article investigates the optimal consensus problem for general linear multiagent
systems (MASs) via a dynamic event-triggered approach. First, a modified interaction-related …

Multi-agent pathfinding (MAPF) is a problem that involves finding a set of non-conflicting
paths for a set of agents confined to a graph. In this work, we study a MAPF setting, where …

Communication learning is an important research direction in the multiagent reinforcement
learning (MARL) domain. Graph neural networks (GNNs) can aggregate the information of …