[3R5-OS-31] OS-31

[3R5-OS-31-03] HTN-RL: Designable Game Character AI Using Hierarchical Task Network and Reinforcement Learning

〇Ray Ito Ito¹, Youichiro Miyake¹ (1. The University of Tokyo )

Keywords:Game AI, Character AI, Reinforcement Learning, hierarchical task network

In the game industry, artificial intelligence (AI) is widely used to control non-player characters (NPCs) as opponents or allies. While deterministic AI methods such as Behavior Trees and Hierarchical Task Networks (HTN) provide developers with precise control over character behaviors, they require extensive manual design, leading to high development costs. Reinforcement learning (RL), which is a nondeterministic method, allows for autonomous behavior generation but offers limited direct control for designers. This study explores the potential of combining HTN with deep reinforcement learning (HTN-RL) to balance designability and adaptability. By applying this approach to a Unity-based MOBA game, we examined its feasibility and effectiveness through observations from 18 international participants. The results suggest that HTN-RL may reduce manual tuning efforts while maintaining design flexibility. Furthermore, we investigated the transferability of learned behavior nodes between different HTNs, highlighting the potential for reusability in various game contexts.