[2511.21934] Heterogeneous Multi-Agent Reinforcement Learning with Attention for Cooperative and Scalable Feature Transformation

Computer Science > Machine Learning arXiv:2511.21934 (cs) [Submitted on 26 Nov 2025 (v1), last revised 27 Feb 2026 (this version, v2)] Title:Heterogeneous Multi-Agent Reinforcement Learning with Attention for Cooperative and Scalable Feature Transformation Authors:Tao Zhe, Huazhen Fang, Kunpeng Liu, Qian Lou, Tamzidul Hoque, Dongjie Wang View a PDF of the paper titled Heterogeneous Multi-Agent Reinforcement Learning with Attention for Cooperative and Scalable Feature Transformation, by Tao Zhe and 4 other authors View PDF HTML (experimental) Abstract:Feature transformation enhances downstream task performance by generating informative features through mathematical feature crossing. Despite the advancements in deep learning, feature transformation remains essential for structured data, where deep models often struggle to capture complex feature interactions. Prior literature on automated feature transformation has achieved success but often relies on heuristics or exhaustive searches, leading to inefficient and time-consuming processes. Recent works employ reinforcement learning (RL) to enhance traditional approaches through a more effective trial-and-error way. However, two limitations remain: 1) Dynamic feature expansion during the transformation process, which causes instability and increases the learning complexity for RL agents; 2) Insufficient cooperation and communication between agents, which results in suboptimal feature crossing operations and degraded model perfo...