Summary of Personalized Dynamic Difficulty Adjustment — Imitation Learning Meets Reinforcement Learning, by Ronja Fuchs et al.
Personalized Dynamic Difficulty Adjustment – Imitation Learning Meets Reinforcement Learning
by Ronja Fuchs, Robin Gieseke, Alexander Dockhorn
First submitted to arxiv on: 13 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a machine learning-based approach to balancing game difficulty in video games. The goal is to create engaging gaming experiences by matching the game’s challenge level with a player’s skill and commitment. To achieve this, two types of agents are used: one that learns to imitate the player’s behavior and another that is trained to surpass the first agent. This framework enables personalized dynamic difficulty adjustment in the context of AI-controlled fighting games. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses machine learning-based agents to create a game difficulty that matches a player’s current behavior. Two types of agents are used: one learns to imitate the player, while the other is trained to beat it. The goal is to make the game more interesting and fun for players by adjusting the difficulty level based on their skills. |
Keywords
» Artificial intelligence » Machine learning
