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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Institute of Electrical and Electronics Engineers Inc.
2019
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my.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/ |
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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_facet |
Ejaz, M.M. Tang, T.B. Lu, C.-K. |
| 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/ |
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13.211869 |