  Deep Reinforcement Learning (DRL) agents have demonstrated impressive success
in a wide range of game genres. However, existing research primarily focuses on
optimizing DRL competence rather than addressing the challenge of prolonged
player interaction. In this paper, we propose a practical DRL agent system for
fighting games named Sh\=ukai, which has been successfully deployed to Naruto
Mobile, a popular fighting game with over 100 million registered users.
Sh\=ukai quantifies the state to enhance generalizability, introducing
Heterogeneous League Training (HELT) to achieve balanced competence,
generalizability, and training efficiency. Furthermore, Sh\=ukai implements
specific rewards to align the agent's behavior with human expectations.
Sh\=ukai's ability to generalize is demonstrated by its consistent competence
across all characters, even though it was trained on only 13% of them.
Additionally, HELT exhibits a remarkable 22% improvement in sample efficiency.
Sh\=ukai serves as a valuable training partner for players in Naruto Mobile,
enabling them to enhance their abilities and skills.
