Advancing DRL Agents in Commercial Fighting Games: Training, Integration, and Agent-Human Alignment
Chen Zhang, Qiang He, Yuan Zhou, Elvis S. Liu, Hong Wang, Jian Zhao, Yang Wang
Abstract
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 _Shūkai_, which has been successfully deployed to Naruto Mobile, a popular fighting game with over 100 million registered users. _Shūkai_ quantifies the state to enhance generalizability, introducing Heterogeneous League Training (HELT) to achieve balanced competence, generalizability, and training efficiency. Furthermore, _Shūkai_ implements specific rewards to align the agent's behavior with human expectations. _Shūkai_'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. _Shūkai_ serves as a valuable training partner for players in Naruto Mobile, enabling them to enhance their abilities and skills.
BibTeX
@inproceedings{
zhang2024advancing,
title={Advancing {DRL} Agents in Commercial Fighting Games: Training, Integration, and Agent-Human Alignment},
author={Chen Zhang and Qiang He and Yuan Zhou and Elvis S. Liu and Hong Wang and Jian Zhao and Yang Wang},
booktitle={Forty-first International Conference on Machine Learning},
year={2024},
url={https://openreview.net/forum?id=eN1T7I7OpZ}
}