Reinforcement learning (RL) has roots in dynamic programming and it is called
adaptive/approximate dynamic programming (ADP) within the control community. This paper …

The adaptive practical optimal time-varying formation tracking problems of the disturbed
high-order multi-agent systems with a noncooperative leader are considered. Different from …

With the development of IoT, virtual reality, cloud computing, and digital twin technologies,
the advent of metaverse has attracted increasing world attention. Metaverse integrates and …

Existing smart grid security research investigates different attack techniques and cascading
failures from the attackers' viewpoints, while the defenders' or the operators' protection …

This paper is concerned with the distributed formation control problem of multi-quadrotor
unmanned aerial vehicle (UAV) in the framework of event triggering. First, for the position …

In this article, a novel data-based adaptive dynamic programming (ADP) method is
presented to solve the optimal consensus tracking control problem for discrete-time (DT) …

In this article, a novel reinforcement learning (RL) method is developed to solve the optimal
tracking control problem of unknown nonlinear multiagent systems (MASs). Different from …

Multiagent reinforcement learning (MARL) has been extensively used in many applications
for its tractable implementation and task distribution. Learning automata, which can be …

Communicating agents with each other in a distributed manner and behaving as a group are
essential in multi-agent reinforcement learning. However, real-world multi-agent systems …

In this paper, we study output feedback leader-follower consensus problem for multiagent
systems subject to external disturbances and time delays in both input and output. First, we …