On Model Identification Based Optimal Control and It’s Applications to Multi-Agent Learning and Control

This paper reviews recent progress in model identification-based learning and optimal control its applications to multi-agent systems (MASs). First, a class of learning-based method, namely adaptive dynamic programming (ADP), is introduced, the existing results using ADP methods solve problems are reviewed. Then, this investigates various kinds identification analyzes feasibility combining method with unknown systems. In addition, expounds current fields single-agent (SASs) MASs. Finally, some conclusions future directions presented.