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, our AI agent, called Tencent Solo, can defeat top professional human players in full 1v1 games.
Cite
@article{arxiv.1912.09729,
title = {Mastering Complex Control in MOBA Games with Deep Reinforcement Learning},
author = {Deheng Ye and Zhao Liu and Mingfei Sun and Bei Shi and Peilin Zhao and Hao Wu and Hongsheng Yu and Shaojie Yang and Xipeng Wu and Qingwei Guo and Qiaobo Chen and Yinyuting Yin and Hao Zhang and Tengfei Shi and Liang Wang and Qiang Fu and Wei Yang and Lanxiao Huang},
journal= {arXiv preprint arXiv:1912.09729},
year = {2020}
}
Comments
AAAI 2020