Differential graphical game‐based multi‐agent tracking control using integral reinforcement learning

Abstract This paper studies the cooperative tracking control problem of interacted multi‐agent systems (MASs) under undirected communication. Based on differential graphical game theory, the MAS tracking control problem is formulated as an infinite horizon cooperative differential graphical game‐theoretic tracking control framework, where a multi‐objective optimization problem is designed and then cast into a Pareto‐equivalent single‐objective optimization problem using a scalarization method. Necessary and sufficient conditions for the existence of the Pareto‐optimal strategy to the game theoretic tracking control are established, where it has been proven that the solution to the integral Bellman optimality equation leads to Pareto‐optimal strategy. Then, an off‐policy integral reinforcement learning scheme to find optimal control strategy using a pure data‐driven manner is developed, which consumes less computation efforts than the traditional learning scheme. Simulated results are conducted to validate the effectiveness of the proposed game and IRL‐based tracking control method.