Deep Reinforcement Learning: An Overview

TL;DR

This paper provides an overview of deep reinforcement learning, highlighting how deep neural architectures like autoencoders, CNNs, and RNNs enhance reinforcement learning in processing high-dimensional data across various applications.

Contribution

It reviews recent advances in deep reinforcement learning, emphasizing the integration of deep architectures with reinforcement learning frameworks.

Findings

Deep architectures improve reinforcement learning performance.

Successful application in high-dimensional data domains.

Comprehensive overview of recent deep reinforcement learning methods.

Abstract

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.