# Online Game Level Generation from Music

arXiv: https://arxiv.org/abs/2207.05271 · HTML: https://mlsanity.com/paper/2207.05271/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 36 references that are in the map.
- First posted on arXiv 12 Jul 2022.

## Builds on (most cited first, 15 of 15)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Soft Actor-Critic Algorithms and Applications](https://mlsanity.com/paper/1812.05905/) (2018)
- [Procedural Content Generation via Machine Learning (PCGML)](https://mlsanity.com/paper/1702.00539/) (2017)
- [PCGRL: Procedural Content Generation via Reinforcement Learning](https://mlsanity.com/paper/2001.09212/) (2020)
- [Evolving Mario Levels in the Latent Space of a Deep Convolutional Generative Adversarial Network](https://mlsanity.com/paper/1805.00728/) (2018)
- [Deep Learning for Procedural Content Generation](https://mlsanity.com/paper/2010.04548/) (2020)
- [The VGLC: The Video Game Level Corpus](https://mlsanity.com/paper/1606.07487/) (2016)
- [Learning Controllable Content Generators](https://mlsanity.com/paper/2105.02993/) (2021)
- [Experience-Driven PCG via Reinforcement Learning: A Super Mario Bros Study](https://mlsanity.com/paper/2106.15877/) (2021)
- [Dance Dance Convolution](https://mlsanity.com/paper/1703.06891/) (2017)
- [TaikoNation: Patterning-focused Chart Generation for Rhythm Action Games](https://mlsanity.com/paper/2107.12506/) (2021)
- [A Novel CNet-assisted Evolutionary Level Repairer and Its Applications to Super Mario Bros](https://mlsanity.com/paper/2005.06148/) (2020)
- [Condition Driven Adaptive Music Generation for Computer Games](https://mlsanity.com/paper/1306.1746/) (2013)

## Built on by (most cited first, 2 of 2)

- [Measuring Diversity of Game Scenarios](https://mlsanity.com/paper/2404.15192/) (2024)
- [State Space Closure: Revisiting Endless Online Level Generation via Reinforcement Learning](https://mlsanity.com/paper/2212.02951/) (2022)

## Abstract (arXiv)

Game consists of multiple types of content, while the harmony of different content types play an essential role in game design. However, most works on procedural content generation consider only one type of content at a time. In this paper, we propose and formulate online level generation from music, in a way of matching a level feature to a music feature in real-time, while adapting to players' play speed. A generic framework named online player-adaptive procedural content generation via reinforcement learning, OPARL for short, is built upon the experience-driven reinforcement learning and controllable reinforcement learning, to enable online level generation from music. Furthermore, a novel control policy based on local search and k-nearest neighbours is proposed and integrated into OPARL to control the level generator considering the play data collected online. Results of simulation-based experiments show that our implementation of OPARL is competent to generate playable levels with difficulty degree matched to the ``energy'' dynamic of music for different artificial players in an online fashion.