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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.07521 |
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| _version_ | 1866908819280363520 |
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| author | Yang, Xionghui Chen, Bozhou Lu, Yunlong Wang, Yongyi Li, Lingfeng Huang, Lanxiao Liu, Lin Wang, Wenjun Meng, Meng Lin, Xia Li, Wenxin |
| author_facet | Yang, Xionghui Chen, Bozhou Lu, Yunlong Wang, Yongyi Li, Lingfeng Huang, Lanxiao Liu, Lin Wang, Wenjun Meng, Meng Lin, Xia Li, Wenxin |
| contents | Recent advances in game AI have demonstrated the feasibility of training agents that surpass top-tier human professionals in 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, production 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_07521 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Pareto-guided Pipeline for Distilling Featherweight AI Agents in Mobile MOBA Games Yang, Xionghui Chen, Bozhou Lu, Yunlong Wang, Yongyi Li, Lingfeng Huang, Lanxiao Liu, Lin Wang, Wenjun Meng, Meng Lin, Xia Li, Wenxin Machine Learning Recent advances in game AI have demonstrated the feasibility of training agents that surpass top-tier human professionals in 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, production 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. |
| title | Pareto-guided Pipeline for Distilling Featherweight AI Agents in Mobile MOBA Games |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2602.07521 |