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

This paper investigates a Q-learning scheme for the optimal consensus control of discrete-
time multiagent systems. The Q-learning algorithm is conducted by reinforcement learning …

This paper introduces a model-free reinforcement learning technique that is used to solve a
class of dynamic games known as dynamic graphical games. The graphical game results …

This article investigates the optimal synchronization problem for unknown discrete-time
nonlinear heterogeneous multiagent systems (MASs). It is very intractable to derive the …

Modeling a system in engineering applications is a time-consuming and labor-intensive
task, as system parameters may change with temperature, component aging, etc. In this …

This paper introduces novel online adaptive Reinforcement Learning approach based on
Policy Iteration for multi-agent systems interacting on graphs. The approach uses reduced …

A novel online adaptive learning technique is developed to solve the dynamic graphical
games in real-time. The players or agents exchange the information on a communication …

This paper introduces novel cooperative control scheme based on adaptive critics for
islanded Microgrids in the presence of disturbances. The interactions between the …

This paper focuses on an optimal consensus problem for heterogeneous discrete-time
nonlinear multi-agent systems (MASs) with partially unknown dynamics. For those systems …

Sustaining the performance of the power grid at a desired operating point is a challenge in
an uncertain environment. As a result of the environment's dynamic and unpredictable …