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

Deheng Ye, Guibin Chen, Peilin Zhao, Fuhao Qiu, Bo Yuan, Wen Zhang, Sheng Chen, Mingfei Sun, Xiaoqian Li, Siqin Li, Jing Liang, Zhenjie Lian (+6 others)
2020 IEEE Transactions on Neural Networks and Learning Systems  
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.
doi:10.1109/tnnls.2020.3029475 pmid:33147150 fatcat:vdpans5ivzgardygdegqjuhwna