# Exploiting the potential of deep reinforcement learning for classification tasks in high-dimensional and unstructured data

arXiv: https://arxiv.org/abs/1912.09595 · HTML: https://mlsanity.com/paper/1912.09595/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2019.

## Built on by (most cited first, 1 of 1)

- [Scope Loss for Imbalanced Classification and RL Exploration](https://mlsanity.com/paper/2308.04024/) (2023)

## Abstract (arXiv)

This paper presents a framework for efficiently learning feature selection policies which use less features to reach a high classification precision on large unstructured data. It uses a Deep Convolutional Autoencoder (DCAE) for learning compact feature spaces, in combination with recently-proposed Reinforcement Learning (RL) algorithms as Double DQN and Retrace.