mlsanityData to 14 Oct 2026

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

arXiv 2503.24099 ↗First posted 31 Mar 2025cs.LGcited by 0

On the map: Deep reinforcement learning methods → RL for games and combinatorial optimization

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.

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.

Builds on

12 of 19 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024PCGRL+: Scaling, Control and Generalization in Reinforcement Learning Level Generators22 Aug
  2. 2024G-PCGRL: Procedural Graph Data Generation via Reinforcement Learning15 Jul

Other references · 10

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2020PCGRL: Procedural Content Generation via Reinforcement Learningcited by 90
  3. 2019Neural MMO: A Massively Multiagent Game Environment for Training and Evaluating Intelligent Agentscited by 44
  4. 2021Learning Controllable Content Generatorscited by 40
  5. 2022Learning Controllable 3D Level Generatorscited by 19
  6. 2019Evolving the Hearthstone Metacited by 8
  7. 2016Demonstrating the Feasibility of Automatic Game Balancingcited by 5
  8. 2023Balancing of competitive two-player Game Levels with Reinforcement Learningcited by 4
  9. 2024GEEvo: Game Economy Generation and Balancing with Evolutionary Algorithmscited by 4
  10. 2024It might be balanced, but is it actually good? An Empirical Evaluation of Game Level Balancingcited by 1

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2025Simulation-Driven Balancing of Competitive Game Levels with Reinforcement Learning
  2. 2025UBCL: A Reinforcement Learning Framework for Controllable and Diverse Player Behaviors
  3. 2025Video Game Level Design as a Multi-Agent Reinforcement Learning Problem
  4. 2024A Framework for Predicting the Impact of Game Balance Changes through Meta Discovery
  5. 2026RuleSmith: Multi-Agent LLMs for Automated Game Balancing
  6. 2025Policy Improvement with Style-Specific Demonstrations
  7. 2024Personalized Dynamic Difficulty Adjustment -- Imitation Learning Meets Reinforcement Learning
  8. 2025The Learning Approach to Games