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, with the goal of maintaining engagement and providing a balanced gameplay experience throughout a match. The approach relies on generating several AI agents through progressively longer training durations, producing distinct and smoothly transitioning difficulty levels that can be switched dynamically. In addition, the system is extended with manually selected parameters that influence physical aspects of the agent’s behavior, such as movement speed, reaction latency, and control precision, complementing the differences arising from decision-making quality. The combined method is potentially applicable to a wide range of video games, and the experimental results demonstrate its effectiveness in producing adaptive and varied opponent behavior.