Learning Diverse Policies in MOBA Games via Macro-Goals
Abstract
Recently, many researchers have made successful progress in building the AI systems for MOBA-game-playing with deep reinforcement learning, such as on Dota 2 and Honor of Kings. Even though these AI systems have achieved or even exceeded human-level performance, they still suffer from the lack of policy diversity. In this paper, we propose a novel Macro-Goals Guided framework, called MGG, to learn diverse policies in MOBA games. MGG abstracts strategies as macro-goals from human demonstrations and trains a Meta-Controller to predict these macro-goals. To enhance policy diversity, MGG samples macro-goals from the Meta-Controller prediction and guides the training process towards these goals. Experimental results on the typical MOBA game Honor of Kings demonstrate that MGG can execute diverse policies in different matches and lineups, and also outperform the state-of-the-art methods over 102 heroes.
Cite
Text
Gao et al. "Learning Diverse Policies in MOBA Games via Macro-Goals." Neural Information Processing Systems, 2021.Markdown
[Gao et al. "Learning Diverse Policies in MOBA Games via Macro-Goals." Neural Information Processing Systems, 2021.](https://mlanthology.org/neurips/2021/gao2021neurips-learning/)BibTeX
@inproceedings{gao2021neurips-learning,
title = {{Learning Diverse Policies in MOBA Games via Macro-Goals}},
author = {Gao, Yiming and Shi, Bei and Du, Xueying and Wang, Liang and Chen, Guangwei and Lian, Zhenjie and Qiu, Fuhao and Han, Guoan and Wang, Weixuan and Ye, Deheng and Fu, Qiang and Yang, Wei and Huang, Lanxiao},
booktitle = {Neural Information Processing Systems},
year = {2021},
url = {https://mlanthology.org/neurips/2021/gao2021neurips-learning/}
}