# Level the Level: Balancing Game Levels for Asymmetric Player Archetypes With Reinforcement Learning

arXiv: https://arxiv.org/abs/2503.24099 · HTML: https://mlsanity.com/paper/2503.24099/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for games and combinatorial optimization, one of 15 directions in Deep reinforcement learning methods.
- Builds on 12 of its 19 references that are in the map.
- First posted on arXiv 31 Mar 2025.

## Placed in

- Deep reinforcement learning methods / [RL for games and combinatorial optimization](https://mlsanity.com/deeprl/rl-for-games-and-combinatorial-optimization/)

## Builds on (most cited first, 12 of 12)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [PCGRL: Procedural Content Generation via Reinforcement Learning](https://mlsanity.com/paper/2001.09212/) (2020)
- [Neural MMO: A Massively Multiagent Game Environment for Training and Evaluating Intelligent Agents](https://mlsanity.com/paper/1903.00784/) (2019)
- [Learning Controllable Content Generators](https://mlsanity.com/paper/2105.02993/) (2021)
- [Learning Controllable 3D Level Generators](https://mlsanity.com/paper/2206.13623/) (2022)
- [Evolving the Hearthstone Meta](https://mlsanity.com/paper/1907.01623/) (2019)
- [PCGRL+: Scaling, Control and Generalization in Reinforcement Learning Level Generators](https://mlsanity.com/paper/2408.12525/) (2024)
- [Demonstrating the Feasibility of Automatic Game Balancing](https://mlsanity.com/paper/1603.03795/) (2016)
- [Balancing of competitive two-player Game Levels with Reinforcement Learning](https://mlsanity.com/paper/2306.04429/) (2023)
- [GEEvo: Game Economy Generation and Balancing with Evolutionary Algorithms](https://mlsanity.com/paper/2404.18574/) (2024)
- [G-PCGRL: Procedural Graph Data Generation via Reinforcement Learning](https://mlsanity.com/paper/2407.10483/) (2024)
- [It might be balanced, but is it actually good? An Empirical Evaluation of Game Level Balancing](https://mlsanity.com/paper/2407.11396/) (2024)

## Abstract (arXiv)

Balancing games, especially those with asymmetric multiplayer content, requires significant manual effort and extensive human playtesting during development. For this reason, this work focuses on generating balanced levels tailored to asymmetric player archetypes, where the disparity in abilities is balanced entirely through the level design. For instance, while one archetype may have an advantage over another, both should have an equal chance of winning. We therefore conceptualize game balancing as a procedural content generation problem and build on and extend a recently introduced method that uses reinforcement learning to balance tile-based game levels. We evaluate the method on four different player archetypes and demonstrate its ability to balance a larger proportion of levels compared to two baseline approaches. Furthermore, our results indicate that as the disparity between player archetypes increases, the required number of training steps grows, while the model's accuracy in achieving balance decreases.