Personalized Dynamic Difficulty Adjustment -- Imitation Learning Meets Reinforcement Learning

TL;DR

This paper introduces a machine learning framework combining imitation learning and reinforcement learning to personalize and dynamically adjust game difficulty based on player behavior, aiming to enhance gaming experience.

Contribution

It proposes a novel approach that integrates imitation and reinforcement learning agents for personalized difficulty adjustment in video games.

Findings

Effective balancing of game difficulty based on player behavior

Successful implementation in fighting game AI context

Potential for improved player engagement

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.