Dynamic Difficulty Adjustment with Machine Learning for Air Hockey

This work presents a method for implementing dynamic difficulty adjustment in the arcade game of Air Hockey using reinforcement learning. The resulting AI-controlled opponent is capable of adapting its skill level to the player’s performance to maintain engagement and provide a balanced gameplay experience. The approach relies on generating several AI agents through progressively longer training durations, resulting in distinct and smoothly transitioning difficulty levels that can be switched dynamically. We discuss how this scheme can be extended with manually selected parameters that influence physical aspects of the agent’s behavior—such as movement speed, reaction latency, and control precision—to complement the variations in decision-making quality. The proposed method is applicable to a wide range of video games, and experimental results demonstrate its effectiveness in producing adaptive and varied opponent behavior.