# An Unsupervised Video Game Playstyle Metric via State Discretization

arXiv: https://arxiv.org/abs/2110.00950 · HTML: https://mlsanity.com/paper/2110.00950/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 32 references that are in the map.
- First posted on arXiv 3 Oct 2021.

## Builds on (most cited first, 10 of 10)

- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [Generating Diverse High-Fidelity Images with VQ-VAE-2](https://mlsanity.com/paper/1906.00446/) (2019)
- [IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures](https://mlsanity.com/paper/1802.01561/) (2018)
- [Rainbow: Combining Improvements in Deep Reinforcement Learning](https://mlsanity.com/paper/1710.02298/) (2017)
- [A Distributional Perspective on Reinforcement Learning](https://mlsanity.com/paper/1707.06887/) (2017)
- [Implicit Quantile Networks for Distributional Reinforcement Learning](https://mlsanity.com/paper/1806.06923/) (2018)
- [Emergent Tool Use From Multi-Agent Autocurricula](https://mlsanity.com/paper/1909.07528/) (2019)
- [Unity: A General Platform for Intelligent Agents](https://mlsanity.com/paper/1809.02627/) (2018)
- [Dopamine: A Research Framework for Deep Reinforcement Learning](https://mlsanity.com/paper/1812.06110/) (2018)
- [An Atari Model Zoo for Analyzing, Visualizing, and Comparing Deep Reinforcement Learning Agents](https://mlsanity.com/paper/1812.07069/) (2018)

## Built on by (most cited first, 3 of 3)

- [Identifying and Clustering Counter Relationships of Team Compositions in PvP Games for Efficient Balance Analysis](https://mlsanity.com/paper/2408.17180/) (2024)
- [Play Style Identification Using Low-Level Representations of Play Traces in MicroRTS](https://mlsanity.com/paper/2507.10172/) (2025)
- [Offline Reinforcement Learning of High-Quality Behaviors Under Robust Style Alignment](https://mlsanity.com/paper/2601.22823/) (2026)

## Abstract (arXiv)

On playing video games, different players usually have their own playstyles. Recently, there have been great improvements for the video game AIs on the playing strength. However, past researches for analyzing the behaviors of players still used heuristic rules or the behavior features with the game-environment support, thus being exhausted for the developers to define the features of discriminating various playstyles. In this paper, we propose the first metric for video game playstyles directly from the game observations and actions, without any prior specification on the playstyle in the target game. Our proposed method is built upon a novel scheme of learning discrete representations that can map game observations into latent discrete states, such that playstyles can be exhibited from these discrete states. Namely, we measure the playstyle distance based on game observations aligned to the same states. We demonstrate high playstyle accuracy of our metric in experiments on some video game platforms, including TORCS, RGSK, and seven Atari games, and for different agents including rule-based AI bots, learning-based AI bots, and human players.