Advancing DRL Agents in Commercial Fighting Games: Training, Integration, and Agent-Human Alignment

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.