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Deep Reinforcement Learning: An Overview

  • Seyed Sajad MousaviEmail author
  • Michael Schukat
  • Enda Howley
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 16)

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 article reviews the recent advances in deep reinforcement learning with 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.

Keywords

Reinforcement learning Deep leaning Neural networks MDPs Observable MDPs 

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Seyed Sajad Mousavi
    • 1
    Email author
  • Michael Schukat
    • 1
  • Enda Howley
    • 1
  1. 1.The College of Engineering and InformaticsNational University of IrelandGalwayRepublic of Ireland

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