Distributed Nash Control of Multiplayer Multiagent Differential Games with Reinforcement Learning

This paper formulates multiplayer multiagent differential games to design the optimal containment control of multiplayer multiagent systems. Distributed Nash equilibrium (simplified as Nash) control policies are designed within the games using only local agents’ trajectories, enabling all players to implement optimal control simultaneously. The solvability of the games and the asymptotic stability of the local error system are ensured. A data-driven integral reinforcement learning (RL) algorithm is devised to compute the distributed Nash control online by leveraging system trajectories without knowing explicit system dynamics. Finally, simulation results verify the effectiveness of the designed games and algorithms.