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Poster
Learning Diverse Policies in MOBA Games via Macro-Goals
Yiming Gao · Bei Shi · Xueying Du · Liang Wang · Guangwei Chen · Zhenjie Lian · Fuhao Qiu · GUOAN HAN · Weixuan Wang · Deheng Ye · Qiang Fu · Wei Yang · Lanxiao Huang

Wed Dec 08 12:30 AM -- 02:00 AM (PST) @
in Poster Session 3 »

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.

Keywords: Reinforcement Learning and Planning

Author Information

Yiming Gao (Tencent AI Lab)
Bei Shi (Tencent AI Lab)
Xueying Du (Tencent AI Lab)
Liang Wang (Tencent)
Guangwei Chen (Tencent AI Lab)
Zhenjie Lian (Tencent AI Lab)
Fuhao Qiu (Tencent AI Lab)
GUOAN HAN (Tencent AI Lab)
Weixuan Wang (Tencent AI Lab)
Deheng Ye (Tencent)
Qiang Fu (Tencent AI Lab)
Wei Yang (Tencent AI Lab)
Lanxiao Huang (Tencent)

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