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

作者：Deheng Ye, Gui-Bin Chen, Peilin Zhao, Fuhao Qiu, Bo Yuan, Wen Zhang, Sheng Chen, Mingfei Sun, Xiaoqian Li, Siqin Li, Jing Liang, Zhenjie Lian, Bei Shi, Liang Wang, Tengfei Shi, Qiang Fu, Wei Yang, Lanxiao Huang · 发表于：IEEE Transactions on Neural Networks and Learning Systems · 年份：2020 · DOI：10.1109/tnnls.2020.3029475 · 被引用次数：54 · 研究领域：Artificial Intelligence in Games、Reinforcement Learning in Robotics、Digital Games and Media

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) games. Unlike prior attempts, we integrate the macro-strategy and the micromanagement of MOBA-game-playing into neural networks in a supervised and end-to-end manner. Tested on Honor of Kings, the most popular MOBA at present, our AI performs competitively at the level of High King players in standard 5v5 games.