Dual-Agent Reinforcement Learning for Automated Feature Generation

2025/05/19 by Wanfu Gao, Gao, Wanfu, Man, Zengyao +4
Computer Science · #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning and Data Classification #Reinforcement Learning in Robotics

paper · pdf · doi:10.48550/arxiv.2505.12628

openalex publication_date 2025/05/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Feature generation involves creating new features from raw data to capture complex relationships among the original features, improving model robustness and machine learning performance. Current methods using reinforcement learning for feature generation have made feature exploration more flexible and efficient. However, several challenges remain: first, during feature expansion, a large number of redundant features are generated. When removing them, current methods only retain the best features each round, neglecting those that perform poorly initially but could improve later. Second, the state representation used by current methods fails to fully capture complex feature relationships. Third, there are significant differences between discrete and continuous features in tabular data, requiring different operations for each type. To address these challenges, we propose a novel dual-agent reinforcement learning method for feature generation. Two agents are designed: the first generates new features, and the second determines whether they should be preserved. A self-attention mechanism enhances state representation, and diverse operations distinguish interactions between discrete and continuous features. The experimental results on multiple datasets demonstrate that the proposed method is effective. The code is available at https://github.com/extess0/DARL.

Citations

• TFWT: Tabular Feature Weighting with Transformer
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• Self-Optimizing Feature Transformation
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• The autofeat Python Library for Automated Feature Engineering and\n Selection
• Feature Engineering for Predictive Modeling using Reinforcement Learning
• Attention Is All You Need
• Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation
• A Critical Review of Recurrent Neural Networks for Sequence Learning
• Playing Atari with Deep Reinforcement Learning
• Robust Principal Component Analysis?
• Robust principal component analysis?

Related

• Automation and Feature Selection Enhancement with Reinforcement Learning (RL)
• Heterogeneous Multi-Agent Reinforcement Learning with Attention for Cooperative and Scalable Feature Transformation
• Traceable Group-Wise Self-Optimizing Feature Transformation Learning: A Dual Optimization Perspective
• Reinforcement Learning-based Feature Generation Algorithm for Scientific Data
• Feature and Instance Joint Selection: A Reinforcement Learning Perspective