This paper investigates the optimal consensus control problem for discrete-time multi-agent
systems with completely unknown dynamics by utilizing a data-driven reinforcement …

The aim of this paper is to provide an overview, although unnecessarily complete, of recent
advances in optimal control, optimization, and game theory for networked systems. First, the …

In this paper, an event-triggered optimal tracking control of discrete-time multi-agent systems
is addressed by using reinforcement learning. In contrast to traditional reinforcement …

This note proposes a novel data-driven solution to the cooperative adaptive optimal control
problem of leader-follower multiagent systems under switching network topology. The …

This survey paper studies deterministic control systems that integrate three of the most active
research areas during the last years:(1) online learning control systems,(2) distributed …

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

This article considers a distributed reinforcement learning problem for decentralized linear
quadratic (LQ) control with partial state observations and local costs. We propose a zero …

In this paper, the optimal bipartite consensus control (OBCC) problem is investigated for
unknown multi-agent systems (MASs) with coopetition networks. A novel distributed OBCC …

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 …

In this paper, the neural-network (NN)-based consensus control problem is investigated for a
class of discrete-time nonlinear multiagent systems (MASs) with a leader subject to input …