[1E5-GS-5] Agents:

[1E5-GS-5-01] Shared Control between Human Player and Fighting Game AI via Policy Ensemble

〇Takumi Yamamoto¹, Yuichi Sei¹, Yasuyuki Tahara¹, Akihiko Ohsuga¹ (1. The University of Electro-Communications)

Keywords:Deep Reinforcement Learning, Computer game, Human-AI Collaboration, Fighting Game, Zero-shot collaboration

In recent years, not only mastering the games but also other fields have also been attracted regarding reinforcement learning. Cooperative game AI research has mainly focused on multiplayer games. However, collaboration tasks that humans and AI control one character have not been widely studied. This study focuses on cooperative manipulation of the fighting game character. We have proposed an AI that supports people in the fighting game:support AI. However, since only one random player was used for training support AI, it couldn ’t cooperate well with the players. Therefore, we used three different types of players in the training of support AI. These players weren ’t random but attack, balance, and defense AIs, each of which trained with different rewards. In the experiment, we asked subjects to use support AIs. there wasn’t much different between the support AI and the proposed method at the result of the Subjective evaluation. However, game scores were shown to be about 7.7% higher for the the proposed method