# Deep Reinforcement Learning: An Overview

arXiv: https://arxiv.org/abs/1806.08894 · HTML: https://mlsanity.com/paper/1806.08894/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jun 2018.

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

- [A comprehensive survey on computational learning methods for analysis of gene expression data](https://mlsanity.com/paper/2202.02958/) (2022)

## Abstract (arXiv)

In recent years, a specific machine learning method called deep learning has gained huge attraction, as it has obtained astonishing results in broad applications such as pattern recognition, speech recognition, computer vision, and natural language processing. Recent research has also been shown that deep learning techniques can be combined with reinforcement learning methods to learn useful representations for the problems with high dimensional raw data input. This chapter reviews the recent advances in deep reinforcement learning with a focus on the most used deep architectures such as autoencoders, convolutional neural networks and recurrent neural networks which have successfully been come together with the reinforcement learning framework.