Efficient Parallel Design for Self-Play in Two-Player Zero-Sum Games
Hongsong Tang, Bo Chen, Yingzhuo Liu, Kuoye Han, Jingqian Liu, Zhaowei Qu · Symmetry · 2025
Self-play methods have achieved remarkable success in two-player zero-sum games, attaining superhuman performance in many complex game domains. Parallelizing learners is a feasible approach to handle complex games. However, parallelizing learners often leads to the suboptimal exploitation of computational resources, resulting in inefficiencies. This paper introduces the Mixed Hierarchical Oracle (MHO), which is designed to enhance training efficiency and performance in complex two-player zero-sum games. MHO efficiently leverages interaction data among parallelized solvers during the Parallelized Oracle (PO) process, while employing Model Soups (MS) to consolidate fragmented computational resources and Hierarchical Exploration (HE) to balance exploration and exploitation. These carefully designed enhancements for parallelized systems significantly improve the training performance of self-play. Additionally, MiniStar is introduced as an open source environment focused on small-scale combat scenarios, developed to facilitate research in self-play algorithms. The MHO is evaluated on both the AlphaStar888 matrix game and MiniStar environment, and ablation studies further demonstrates its effectiveness in improving the agent’s decision-making capabilities. This work highlight the potential of the MHO to optimize compute resource utilization and improve performance in self-play methods.