Mastering Complex Control in MOBA Games with Deep Reinforcement Learning
Abstract
We study the reinforcement learning problem of complex action control in the Multi-player Online Battle Arena (MOBA) 1v1 games. This problem involves far more complicated state and action spaces than those of traditional 1v1 games, such as Go and Atari series, which makes it very difficult to search any policies with human-level performance. In this paper, we present a deep reinforcement learning framework to tackle this problem from the perspectives of both system and algorithm. Our system is of low coupling and high scalability, which enables efficient explorations at large scale. Our algorithm includes several novel strategies, including control dependency decoupling, action mask, target attention, and dual-clip PPO, with which our proposed actor-critic network can be effectively trained in our system. Tested on the MOBA game Honor of Kings, the trained AI agents can defeat top professional human players in full 1v1 games.
Cite
Text
Ye et al. "Mastering Complex Control in MOBA Games with Deep Reinforcement Learning." AAAI Conference on Artificial Intelligence, 2020. doi:10.1609/AAAI.V34I04.6144Markdown
[Ye et al. "Mastering Complex Control in MOBA Games with Deep Reinforcement Learning." AAAI Conference on Artificial Intelligence, 2020.](https://mlanthology.org/aaai/2020/ye2020aaai-mastering/) doi:10.1609/AAAI.V34I04.6144BibTeX
@inproceedings{ye2020aaai-mastering,
title = {{Mastering Complex Control in MOBA Games with Deep Reinforcement Learning}},
author = {Ye, Deheng and Liu, Zhao and Sun, Mingfei and Shi, Bei and Zhao, Peilin and Wu, Hao and Yu, Hongsheng and Yang, Shaojie and Wu, Xipeng and Guo, Qingwei and Chen, Qiaobo and Yin, Yinyuting and Zhang, Hao and Shi, Tengfei and Wang, Liang and Fu, Qiang and Yang, Wei and Huang, Lanxiao},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2020},
pages = {6672-6679},
doi = {10.1609/AAAI.V34I04.6144},
url = {https://mlanthology.org/aaai/2020/ye2020aaai-mastering/}
}