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.