Autonomous Visual Navigation using Deep Reinforcement Learning: An Overview

• Main Authors: Ejaz, M.M., Tang, T.B., Lu, C.-K.
• Format: Conference or Workshop Item
• 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/23548/

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.