Personalized Dynamic Difficulty Adjustment Imitation Learning Meets Reinforcement Learning

authored by: Ronja Fuchs, Robin Gieseke, Alexander Dockhorn
Abstract: Balancing game difficulty in video games is a key task to create interesting gaming experiences for players. Mismatching the game difficulty and a player's skill or commitment results in frustration or boredom on the player's side, and hence reduces time spent playing the game. In this work, we explore balancing game difficulty using machine learning-based agents to challenge players based on their current behavior. This is achieved by a combination of two agents, in which one learns to imitate the player, while the second is trained to beat the first. In our demo, we investigate the proposed framework for personalized dynamic difficulty adjustment of AI agents in the context of the fighting game AI competition.
Organisation(s): Institute of Information Processing
Type: Conference contribution
Publication date: 05.08.2024
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Artificial Intelligence, Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition, Human-Computer Interaction, Software
Electronic version(s): https://doi.org/10.48550/arXiv.2408.06818 (Access: Open)
https://doi.org/10.1109/CoG60054.2024.10645659 (Access: Closed)

BibTeX

@inproceedings{12ab9e751db84b5792fb825107e9d464,
title = "Personalized Dynamic Difficulty Adjustment Imitation Learning Meets Reinforcement Learning",
abstract = "Balancing game difficulty in video games is a key task to create interesting gaming experiences for players. Mismatching the game difficulty and a player's skill or commitment results in frustration or boredom on the player's side, and hence reduces time spent playing the game. In this work, we explore balancing game difficulty using machine learning-based agents to challenge players based on their current behavior. This is achieved by a combination of two agents, in which one learns to imitate the player, while the second is trained to beat the first. In our demo, we investigate the proposed framework for personalized dynamic difficulty adjustment of AI agents in the context of the fighting game AI competition.",
keywords = "Dynamic Difficulty Adjustment, Fighting Game AI, Imitation Learning, Reinforcement Learning",
author = "Ronja Fuchs and Robin Gieseke and Alexander Dockhorn",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 6th Annual IEEE Conference on Games, CoG 2024 ; Conference date: 05-08-2024 Through 08-08-2024",
year = "2024",
month = aug,
day = "5",
doi = "10.48550/arXiv.2408.06818",
language = "English",
isbn = "979-8-3503-5068-5",
series = "IEEE Conference on Computatonal Intelligence and Games, CIG",
publisher = "IEEE Computer Society",
booktitle = "Proceedings of the 2024 IEEE Conference on Games, CoG 2024",
address = "United States",
}