Value Iteration-Based Cooperative Adaptive Optimal Control for Multi-Player Differential Games With Incomplete Information

This paper presents a novel comperative value iteration (VI)-based adaptive dynamic programming method for multi-player differential game models with a convergence proof. The players are divided into two groups in the learning process and adapt their policies sequentially. Our method removes the dependence of admissible initial policies, which is one of the main drawbacks of the PI-based frameworks. Furthermore, this algorithm enables the players to adapt their control policies without full knowledge of others' system parameters or control laws. The efficacy of our method is illustrated by three examples.