Novel architecture combines reinforcement learning with simulation to automatically balance competitive game levels
We are pleased to announce the publication of “Simulation-Driven Balancing of Competitive Game Levels with Reinforcement Learning” by Florian Rupp, Manuel Eberhardinger, and Kai Eckert in IEEE Transactions on Games (Volume 16, pages 903-913, 2024).
Automating a Labor-Intensive Process
The balancing of game levels in competitive two-player contexts traditionally involves extensive manual work and testing, particularly for non-symmetrical game levels. Game developers must conduct numerous playtesting sessions to ensure that neither player has an unfair advantage due to level design, a process that can be both time-consuming and expensive.
Framing Balance as Content Generation
This research takes an innovative approach by framing game balancing as a procedural content generation task within the PCGRL (Procedural Content Generation via Reinforcement Learning) framework. This conceptual shift enables the application of machine learning techniques to automatically balance tile-based levels.
Three-Part Architecture
The proposed architecture consists of three integrated components:
- Level Generator: Creates the initial game level layouts
- Balancing Agent: Uses reinforcement learning to modify levels toward balance
- Reward Modeling Simulation: Evaluates level balance through repeated gameplay simulations
Through iterative simulations, the balancing agent receives rewards for adjusting levels toward specified balancing objectives, such as achieving equal win rates for all players.
Novel Swap-Based Representations
A key technical innovation is the introduction of new swap-based representations that improve the robustness of playability. These representations enable agents to balance game levels more effectively and quickly compared to traditional PCGRL approaches. By swapping elements rather than regenerating entire sections, the system maintains playable level structures throughout the optimization process.
Demonstrated Effectiveness
The research demonstrates that this approach can successfully balance non-symmetrical competitive game levels automatically, reducing the need for extensive manual iteration and playtesting. This has practical implications for game development studios, potentially accelerating development timelines and reducing costs associated with level design and balancing.
Future Directions
This work opens avenues for further research in automated game design, including potential applications to different game genres, more complex balancing objectives, and integration with human-in-the-loop design processes.
Citation: Florian Rupp, Manuel Eberhardinger, Kai Eckert (2024): Simulation-Driven Balancing of Competitive Game Levels with Reinforcement Learning. In IEEE Transactions on Games, Vol. 16, pp. 903-913.