Initially Excited Learning of Discrete-Time Multiplayer Games

• 成果类型: Article
• 署名作者: Wu, Jiacheng; Wang, Jing; Ma, Qian; Shen, Hao
• 署名单位: Zhejiang University; Anhui University of Technology; Nanjing University of Science & Technology
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2025.3609513
• 发表日期: 2026
• 关键词: GRAPHICAL GAMES; algorithms

摘要

Data-storage-based reinforcement learning (RL) algorithms for dynamic games typically impose a persistent excitation (PE) condition to guarantee convergence. However, this requirement can be overly restrictive in practical implementations. To address this limitation, this article proposes a novel online RL algorithm for approximating the Nash equilibrium solutions in discrete-time multiplayer nonzero-sum games under a relaxed initial excitation condition, thereby avoiding the need for the PE assumption. Different from existing methods that rely on data storage mechanisms and full-rank conditions, the proposed algorithm incorporates an online verification scheme that evaluates the positivity properties of an augmented matrix. This mechanism ensures initial excitation without requiring heuristic data collection designed to satisfy the PE condition. Furthermore, we provide a rigorous analysis to establish the convergence of the designed algorithm. Finally, comparative studies validate the effectiveness of the proposed approach.