Honor of Kings Arena: an Environment for Generalization in Competitive Reinforcement Learning

Hua Wei, Jingxiao Chen, Xiyang Ji, Hongyang Qin, Minwen Deng, Siqin Li, Liang Wang, Weinan Zhang, Yong Yu, Liu Linc, Lanxiao Huang, Deheng Ye, QIANG FU, Yang Wei

Keywords: Reinforcement learning, competitive reinforcement learning

Abstract: This paper introduces Honor of Kings Arena, a reinforcement learning (RL) environment based on the Honor of Kings, one of the world’s most popular games at present. Compared to other environments studied in most previous work, ours presents new generalization challenges for competitive reinforcement learning. It is a multi-agent problem with one agent competing against its opponent; and it requires the generalization ability as it has diverse targets to control and diverse opponents to compete with. We describe the observation, action, and reward specifications for the Honor of Kings domain and provide an open-source Python-based interface for communicating with the game engine. We provide twenty target heroes with a variety of tasks in Honor of Kings Arena and present initial baseline results for RL-based methods with feasible computing resources. Finally, we showcase the generalization challenges imposed by Honor of Kings Arena and possible remedies to the challenges. All of the software, including the environment-class, are publicly available.

Author Statement: Yes

URL: https://github.com/tencent-ailab/hok_env

License:

Apache License 2.0 for `hok_env`

Tencent's Hornor of Kings AI And Machine Learning License for the game engine: https://github.com/tencent-ailab/hok_env/blob/master/GAMECORE.LICENSE

Contribution Process Agreement: Yes

In Person Attendance: Yes

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:2209.08483/code)