TY  - CONF
A1  - Ejaz, M.M.
A1  - Tang, T.B.
A1  - Lu, C.-K.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075634481&doi=10.1109%2fSCORED.2019.8896352&partnerID=40&md5=d1e260ce7a22c03ad23ec467fabaa366
EP  - 299
Y1  - 2019///
PB  - Institute of Electrical and Electronics Engineers Inc.
SN  - 9781728126135
N1  - cited By 4; Conference of 17th IEEE Student Conference on Research and Development, SCOReD 2019 ; Conference Date: 15 October 2019 Through 17 October 2019; Conference Code:154444
N2  - 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.
SP  - 294
ID  - scholars11247
TI  - Autonomous Visual Navigation using Deep Reinforcement Learning: An Overview
KW  - Computer games; Learning algorithms; Machine learning; Navigation; Reinforcement learning; Robots
KW  -  Autonomous navigation; Complex problems; Human expert; Human levels; Learning techniques; Raw images; Video game; Visual Navigation
KW  -  Deep learning
AV  - none
ER  -