Personalized Dynamic Difficulty Adjustment -- Imitation Learning Meets Reinforcement Learning

2024/08/13 by Ronja Fuchs, Fuchs, Ronja, Robin Gieseke +3 · 1 citation
Computer Science · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Reinforcement Learning in Robotics

paper · pdf · doi:10.48550/arxiv.2408.06818

openalex publication_date 2024/08/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Cited by

• A modular framework for automated evaluation of procedural content generation in serious games with deep reinforcement learning agents

Related

• Creating Pro-Level AI for a Real-Time Fighting Game Using Deep Reinforcement Learning
• Diversity-based Deep Reinforcement Learning Towards Multidimensional Difficulty for Fighting Game AI
• Player Dominance Adjustment in Games
• Continuous Reinforcement Learning-based Dynamic Difficulty Adjustment in a Visual Working Memory Game
• Dynamic Difficulty Adjustment on MOBA Games