Timezone: »

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 · Wei Yang

Thu Dec 01 02:00 PM -- 04:00 PM (PST) @ Hall J #1027

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 Information

Hua Wei (New Jersey Institute of Technology)
Jingxiao Chen (Shanghai Jiao Tong University)
Xiyang Ji (Tencent AI Lab)
Hongyang Qin (South China University of Technology)
Minwen Deng (Tencent AI Lab)
Siqin Li (Tencent AI Lab)
Liang Wang (Tencent)
Weinan Zhang (Shanghai Jiao Tong University)
Yong Yu (Shanghai Jiao Tong Unviersity)
Liu Linc (University of Science and Technology of China)
Lanxiao Huang (Tencent)
Deheng Ye (Tencent)
Qiang Fu (Tencent AI Lab)
Wei Yang (Tencent AI Lab)

More from the Same Authors