Off-policy learning for adaptive optimal output synchronization of heterogeneous multi-agent systems

摘要

This paper proposes an off-policy learning-based dynamic state feedback protocol that achieves the optimal synchronization of heterogeneous multi-agent systems (MAS) over a directed communication network. Note that most of the recent works on heterogeneous MAS are not formed in an optimal manner. By formulating the cooperative output regulation problem as an H-infinity optimization problem, we can use reinforcement learning to find output synchronization protocols online along with the system trajectories without solving output regulator equations. In contrast to the existing optimal literature where leader's states are assumed to be globally or distributively available for the communication, we only allow the relative system outputs to transmit through the network; namely, no leader's states are needed now for the control or learning purpose. (C) 2020 Elsevier Ltd. All rights reserved.