Creating a Foundation for Dynamic Difficulty Adjustment within PCG of games using Imitation Learning

Abstract— This research proposes a novel approach to create a foundation for dynamic difficulty adjustment (DDA) within computer games that use procedural content generation (PCG), utilizing imitation learning to optimize gameplay. When PCG is used in creating the levels and enemies within a game, the difficulty adjustment must be ensured so that the game is not too hard or too easy for each player. However, PCG is random by nature, and thus, the developers may have a challenging task of adjusting the difficulty for each player in such games. The study aims to address these limitations by developing a foundation for DDA models based on imitation learning. The proposed model incorporates an imitation learning component, referred to as the 'Clone,' which replicates the player’s behavior, alongside an enemy creator agent that leverages procedural content generation (PCG) to design enemies. By analyzing the Clone's performance against these procedurally generated enemies, the system ensures the creation of fair and engaging levels. To this end, a ۲D platformer Unity game using PCG was developed, and imitation learning was utilized through Unity's ML-agents module. These models were used to mimic the players' play-style to predict the player's performance in PCG-generated levels. Three separate models were created to mimic five players. It was observed that two of these models could mimic players' performance, showing that this method can be used to implement DDA.

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Keywords— Dynamic difficulty adjustment ، Procedural content generation ، Imitation learning ، Computer Games