# Automation and Feature Selection Enhancement with Reinforcement Learning (RL)

arXiv: https://arxiv.org/abs/2503.11991 · HTML: https://mlsanity.com/paper/2503.11991/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 9 of its 15 references that are in the map.
- First posted on arXiv 15 Mar 2025.

## Placed in

- Deep reinforcement learning methods / [Deep RL algorithms and representations](https://mlsanity.com/deeprl/deep-rl-algorithms-and-representations/)

## Builds on (most cited first, 9 of 9)

- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Group-wise Reinforcement Feature Generation for Optimal and Explainable Representation Space Reconstruction](https://mlsanity.com/paper/2205.14526/) (2022)
- [AutoFS: Automated Feature Selection via Diversity-aware Interactive Reinforcement Learning](https://mlsanity.com/paper/2008.12001/) (2020)
- [Efficient Reinforced Feature Selection via Early Stopping Traverse Strategy](https://mlsanity.com/paper/2109.14180/) (2021)
- [Interactive Reinforcement Learning for Feature Selection with Decision Tree in the Loop](https://mlsanity.com/paper/2010.02506/) (2020)
- [Simplifying Reinforced Feature Selection via Restructured Choice Strategy of Single Agent](https://mlsanity.com/paper/2009.09230/) (2020)
- [Feature Interaction Aware Automated Data Representation Transformation](https://mlsanity.com/paper/2309.17011/) (2023)
- [Self-Optimizing Feature Transformation](https://mlsanity.com/paper/2209.08044/) (2022)
- [Feature and Instance Joint Selection: A Reinforcement Learning Perspective](https://mlsanity.com/paper/2205.07867/) (2022)

## Built on by (most cited first, 2 of 2)

- [Context Attribution with Multi-Armed Bandit Optimization](https://mlsanity.com/paper/2506.19977/) (2025)
- [A Multi-Component Reward Function with Policy Gradient for Automated Feature Selection with Dynamic Regularization and Bias Mitigation](https://mlsanity.com/paper/2510.09705/) (2025)

## Abstract (arXiv)

Effective feature selection, representation and transformation are principal steps in machine learning to improve prediction accuracy, model generalization and computational efficiency. Reinforcement learning provides a new perspective towards balanced exploration of optimal feature subset using multi-agent and single-agent models. Interactive reinforcement learning integrated with decision tree improves feature knowledge, state representation and selection efficiency, while diversified teaching strategies improve both selection quality and efficiency. The state representation can further be enhanced by scanning features sequentially along with the usage of convolutional auto-encoder. Monte Carlo-based reinforced feature selection(MCRFS), a single-agent feature selection method reduces computational burden by incorporating early-stopping and reward-level interactive strategies. A dual-agent RL framework is also introduced that collectively selects features and instances, capturing the interactions between them. This enables the agents to navigate through complex data spaces. To outperform the traditional feature engineering, cascading reinforced agents are used to iteratively improve the feature space, which is a self-optimizing framework. The blend of reinforcement learning, multi-agent systems, and bandit-based approaches offers exciting paths for studying scalable and interpretable machine learning solutions to handle high-dimensional data and challenging predictive tasks.