Heterogeneous Multi-Agent Reinforcement Learning with Attention for Cooperative and Scalable Feature Transformation

Researchers propose a new multi-agent reinforcement learning framework that uses three cooperative agents with attention mechanisms to automate feature transformation for machine learning models. The approach addresses key limitations in existing automated feature engineering methods, including dynamic feature expansion instability and insufficient agent cooperation.