Supervised learning in flocking simulations

Supervised learning in flocking simulations involves training an algorithm to imitate expert behavior by providing it with example scenarios. The system learns patterns—such as how birds or robots align, stay close, or avoid obstacles—by analyzing labeled data of correct actions. Once trained, the model can predict appropriate responses for new situations, enabling realistic flocking behavior. This approach helps create more natural and efficient group movements by learning from demonstrated strategies, improving the simulation’s accuracy without hardcoding every rule manually.