Personalized Dynamic Difficulty Adjustment -- Imitation Learning Meets Reinforcement Learning
This addresses the problem of player frustration or boredom due to mismatched game difficulty, potentially improving engagement for gamers, though it appears incremental as it builds on existing machine learning methods.
The paper tackles personalized dynamic difficulty adjustment in video games by combining imitation learning and reinforcement learning to create AI agents that adapt to player behavior, achieving a framework tested in a fighting game AI competition.
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.