activity
20202026
most citedSupervised Learning Achieves Human-Level Performance in MOBA Games: A Case Study of Honor of Kings

55 citations · 69 across the 5 of their papers we have counts for

collaborators

5 papers

cs.RO2026

SyncPlan: Long-Horizon LLM Coordination with Explicit Synchronization and Adaptive Correction

Shen You, Xiaoming Zhu, Weining Weng +17

LLM-based multi-agent coordination faces a fundamental trade-off between efficiency and adaptivity in dynamic environments. Existing approaches typically rely on repeated LLM invoc…

cs.HC2024

Enhancing Human Experience in Human-Agent Collaboration: A Human-Centered Modeling Approach Based on Positive Human Gain

Yiming Gao, Feiyu Liu, Liang Wang +12

Existing game AI research mainly focuses on enhancing agents' abilities to win games, but this does not inherently make humans have a better experience when collaborating with thes…

cs.AI2023★ 2 cited

Towards Effective and Interpretable Human-Agent Collaboration in MOBA Games: A Communication Perspective

Yiming Gao, Feiyu Liu, Liang Wang +11

MOBA games, e.g., Dota2 and Honor of Kings, have been actively used as the testbed for the recent AI research on games, and various AI systems have been developed at the human leve…

cs.LG2022★ 12 cited

Honor of Kings Arena: an Environment for Generalization in Competitive Reinforcement Learning

Hua Wei, Jingxiao Chen, Xiyang Ji +11

This paper introduces Honor of Kings Arena, a reinforcement learning (RL) environment based on Honor of Kings, one of the world's most popular games at present. Compared to other e…

cs.AI2020★ 55 cited

Supervised Learning Achieves Human-Level Performance in MOBA Games: A Case Study of Honor of Kings

Deheng Ye, Guibin Chen, Peilin Zhao +15

We present JueWu-SL, the first supervised-learning-based artificial intelligence (AI) program that achieves human-level performance in playing multiplayer online battle arena (MOBA…