Advancing DRL Agents in Commercial Fighting Games: Training, Integration, and Agent-Human Alignment
<|reference_start|>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.<|reference_end|>
@article{zhang2024advancing,
    title={Advancing DRL Agents in Commercial Fighting Games: Training,
  Integration, and Agent-Human Alignment},
    author={Chen Zhang, Qiang He, Zhou Yuan, Elvis S. Liu, Hong Wang, Jian Zhao,
  Yang Wang},
    journal={arXiv preprint arXiv:2406.01103},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.01103},
    primaryClass={cs.AI cs.HC cs.LG}
}