Autonomous Visual Navigation using Deep Reinforcement Learning: An Overview

Reinforcement Learning (RL) algorithm with deep learning techniques helps to solve many complex problems of today's world, such as to play a video game and autonomous navigation in the robots using the raw image as an input. Deep learning provides the mechanism to RL which enables the agent to...

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Main Authors: Ejaz, M.M., Tang, T.B., Lu, C.-K.
Format: Conference or Workshop Item
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.24903 /
Published: Institute of Electrical and Electronics Engineers Inc. 2019
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075634481&doi=10.1109%2fSCORED.2019.8896352&partnerID=40&md5=d1e260ce7a22c03ad23ec467fabaa366
http://eprints.utp.edu.my/24903/
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id utp-eprints.24903
recordtype eprints
spelling utp-eprints.249032021-08-27T06:39:43Z Autonomous Visual Navigation using Deep Reinforcement Learning: An Overview Ejaz, M.M. Tang, T.B. Lu, C.-K. Reinforcement Learning (RL) algorithm with deep learning techniques helps to solve many complex problems of today's world, such as to play a video game and autonomous navigation in the robots using the raw image as an input. Deep learning provides the mechanism to RL which enables the agent to solve the human level task. The rise of RL begins when a computer player beat the human expert in the most difficult game Go 6. In this paper, we discuss some important topics such as the general view of reinforcement learning, methods, and algorithms of reinforcement learning and challenges which reinforcement learning is facing. Finally, we discussed a survey of implemented algorithms of RL in the field of robotics for autonomous visual navigation. © 2019 IEEE. Institute of Electrical and Electronics Engineers Inc. 2019 Conference or Workshop Item NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075634481&doi=10.1109%2fSCORED.2019.8896352&partnerID=40&md5=d1e260ce7a22c03ad23ec467fabaa366 Ejaz, M.M. and Tang, T.B. and Lu, C.-K. (2019) Autonomous Visual Navigation using Deep Reinforcement Learning: An Overview. In: UNSPECIFIED. http://eprints.utp.edu.my/24903/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description Reinforcement Learning (RL) algorithm with deep learning techniques helps to solve many complex problems of today's world, such as to play a video game and autonomous navigation in the robots using the raw image as an input. Deep learning provides the mechanism to RL which enables the agent to solve the human level task. The rise of RL begins when a computer player beat the human expert in the most difficult game Go 6. In this paper, we discuss some important topics such as the general view of reinforcement learning, methods, and algorithms of reinforcement learning and challenges which reinforcement learning is facing. Finally, we discussed a survey of implemented algorithms of RL in the field of robotics for autonomous visual navigation. © 2019 IEEE.
format Conference or Workshop Item
author Ejaz, M.M.
Tang, T.B.
Lu, C.-K.
spellingShingle Ejaz, M.M.
Tang, T.B.
Lu, C.-K.
Autonomous Visual Navigation using Deep Reinforcement Learning: An Overview
author_sort Ejaz, M.M.
title Autonomous Visual Navigation using Deep Reinforcement Learning: An Overview
title_short Autonomous Visual Navigation using Deep Reinforcement Learning: An Overview
title_full Autonomous Visual Navigation using Deep Reinforcement Learning: An Overview
title_fullStr Autonomous Visual Navigation using Deep Reinforcement Learning: An Overview
title_full_unstemmed Autonomous Visual Navigation using Deep Reinforcement Learning: An Overview
title_sort autonomous visual navigation using deep reinforcement learning: an overview
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2019
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075634481&doi=10.1109%2fSCORED.2019.8896352&partnerID=40&md5=d1e260ce7a22c03ad23ec467fabaa366
http://eprints.utp.edu.my/24903/
_version_ 1741196887334060032
score 11.62408

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