Reinforcement Learning: A Friendly Introduction

Dema Daoun, Fabiha Ibnat, Zulfikar Alom, Zeyar Aung, Mohammad Abdul Azim

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

Reinforcement Learning (RL) is a branch of machine learning (ML) that is used to train artificial intelligence (AI) systems and find the optimal solution for problems. This tutorial paper aims to present an introductory overview of the RL. Furthermore, we discuss the most popular algorithms used in RL and the Markov decision process (MDP) usage in the RL environment. Moreover, RL applications and achievements that shine in the world of AI are covered.

Original languageBritish English
Title of host publicationThe International Conference on Deep Learning, Big Data and Blockchain, Deep-BDB 2021
EditorsIrfan Awan, Salima Benbernou, Muhammad Younas, Markus Aleksy
PublisherSpringer Science and Business Media Deutschland GmbH
Pages134-146
Number of pages13
ISBN (Print)9783030843366
DOIs
StatePublished - 2022
Event2nd International Conference on Deep Learning, Big Data and Blockchain, Deep-BDB 2021 - Virtual, Online
Duration: 23 Aug 202125 Aug 2021

Publication series

NameLecture Notes in Networks and Systems
Volume309
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

Conference2nd International Conference on Deep Learning, Big Data and Blockchain, Deep-BDB 2021
CityVirtual, Online
Period23/08/2125/08/21

Keywords

  • Artificial intelligence
  • Bellman optimality
  • Markov decision process
  • Reinforcement learning

Fingerprint

Dive into the research topics of 'Reinforcement Learning: A Friendly Introduction'. Together they form a unique fingerprint.

Cite this