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

Numerous research efforts have been conducted to simulate the crowd movements, while
relatively few of them are specifically focused on multihazard situations. In this paper, we …

This article develops a distributed fault-tolerant consensus control (DFTCC) approach for
multiagent systems by using adaptive dynamic programming. By establishing a local fault …

This article investigates the secure consensus problem of general linear multiagent systems
with denial-of-service (DoS) attacks. Owning to the existence of DoS attacks, it is challenging …

This article investigates the leader–follower formation learning control (FLC) problem for
discrete-time strict-feedback multiagent systems (MASs). The objective is to acquire the …

In this article, we propose an algorithm that combines actor–critic-based off-policy method
with consensus-based distributed training to deal with multiagent deep reinforcement …

This article investigates the robust optimal consensus for nonlinear multiagent systems
(MASs) through the local adaptive dynamic programming (ADP) approach and the event …

In this article, the consensus tracking problem is discussed for a class of discrete‐time
nonlinear multi‐agent systems (MASs) subject to denial‐of‐service (DoS) attacks. The …

This article is devoted to investigating the exponential leader-following consensus (ELFC) of
stochastic discrete multi-agent systems (SDMASs) in the presence of parameter …

This paper investigates the model reference adaptive control (MRAC) problem for unknown
nonlinear discrete-time systems, which is how to guarantee adaptation to the variable …