# A Multi-Component Reward Function with Policy Gradient for Automated Feature Selection with Dynamic Regularization and Bias Mitigation

arXiv: https://arxiv.org/abs/2510.09705 · HTML: https://mlsanity.com/paper/2510.09705/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 12 of its 41 references that are in the map.
- First posted on arXiv 9 Oct 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, 12 of 12)

- [Constrained Policy Optimization](https://mlsanity.com/paper/1705.10528/) (2017)
- [A Lyapunov-based Approach to Safe Reinforcement Learning](https://mlsanity.com/paper/1805.07708/) (2018)
- [Efficient Reinforced Feature Selection via Early Stopping Traverse Strategy](https://mlsanity.com/paper/2109.14180/) (2021)
- [Binary Classification for High Dimensional Data using Supervised Non-Parametric Ensemble Method](https://mlsanity.com/paper/2202.07779/) (2022)
- [Geometric graphs from data to aid classification tasks with graph convolutional networks](https://mlsanity.com/paper/2005.04081/) (2020)
- [A comparative study of forecasting Corporate Credit Ratings using Neural Networks, Support Vector Machines, and Decision Trees](https://mlsanity.com/paper/2007.06617/) (2020)
- [Non-linear, Sparse Dimensionality Reduction via Path Lasso Penalized Autoencoders](https://mlsanity.com/paper/2102.10873/) (2021)
- [Optimizing Interpretable Decision Tree Policies for Reinforcement Learning](https://mlsanity.com/paper/2408.11632/) (2024)
- [Feature and Instance Joint Selection: A Reinforcement Learning Perspective](https://mlsanity.com/paper/2205.07867/) (2022)
- [Fair Causal Feature Selection](https://mlsanity.com/paper/2306.10336/) (2023)
- [Automation and Feature Selection Enhancement with Reinforcement Learning (RL)](https://mlsanity.com/paper/2503.11991/) (2025)
- [Inference-Time Rule Eraser: Fair Recognition via Distilling and Removing Biased Rules](https://mlsanity.com/paper/2404.04814/) (2024)

## Abstract (arXiv)

Static feature exclusion strategies often fail to prevent bias when hidden dependencies influence the model predictions. To address this issue, we explore a reinforcement learning (RL) framework that integrates bias mitigation and automated feature selection within a single learning process. Unlike traditional heuristic-driven filter or wrapper approaches, our RL agent adaptively selects features using a reward signal that explicitly integrates predictive performance with fairness considerations. This dynamic formulation allows the model to balance generalization, accuracy, and equity throughout the training process, rather than rely exclusively on pre-processing adjustments or post hoc correction mechanisms. In this paper, we describe the construction of a multi-component reward function, the specification of the agents action space over feature subsets, and the integration of this system with ensemble learning. We aim to provide a flexible and generalizable way to select features in environments where predictors are correlated and biases can inadvertently re-emerge.