Zhanlue Liang

Robust formation tracking of multi-agent systems via reinforcement learning-based hybrid impulsive control.

The Hierarchical Multi-agent Cooperative Reinforcement Learning (HMAC-RL) framework is introduced to optimally and adaptively refine both control parameters and impulsive moments and offers enhanced adaptability, faster corrective actions, and improved system robustness in uncertain and evolving environments.