Pareto-guided Pipeline for Distilling Featherweight AI Agents in Mobile MOBA Games

Xionghui Yang, Bozhou Chen, Yunlong Lu, Yongyi Wang, Lingfeng Li, Lanxiao Huang, Lin Liu, Wenjun Wang, Meng Meng, Xia Lin, Wenxin Li

Keywords: Pareto-Frontier, Distillation, MOBA Game AI

Abstract:

Recent advances in game AI have demonstrated the feasibility of training dominant agents for complex environments such as Honor of Kings (HoK), a leading mobile multiplayer online battle arena (MOBA) game.
However, deploying such powerful agents on mobile devices remains a major challenge.
On one hand, the intricate multi-modal state representation and hierarchical action space of HoK demand large, sophisticated policy networks that are inherently difficult to compress into lightweight forms.
On the other hand, real-time deployment requires high-frequency inference under strict energy and latency constraints on mobile platform.
To the best of our knowledge, bridging large-scale game AI and practical on-device deployment has not been systematically studied.
In this work, we propose a Pareto optimality guided pipeline and design a high-efficiency student architecture search space tailored for mobile execution,
enabling systematic exploration of the trade-off between performance and efficiency.
Experimental results demonstrate that the distilled model achieves remarkable efficiency, including an $12.4\times$ faster inference speed (under 0.5ms per frame) and a $15.6\times$ improvement in energy efficiency (under 0.5mAh per game), while retaining a 40.32\% win rate against the original teacher model.

Area: Engineering and Analysis of Multiagent Systems (EMAS)

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Submission Number: 313