Context-based learning for autonomous vehicles

This research seeks to prove that Deep Recurrent Q-Network (DRQN) approaches are great options for the control of autonomous vehicles. DRQN algorithms are widely used in video game competitions, but not many studies are available for their use in autonomous vehicles. In this paper, we present a cont...

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Bibliographic Details
Published in2020 IEEE 23rd International Symposium on Real-Time Distributed Computing (ISORC) pp. 150 - 151
Main Authors Peixoto, J.P. Maria, Azim, Akramul
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2020
Subjects
autonomous vehicles
DRQN
POMDP
reinforcement learning
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Summary:This research seeks to prove that Deep Recurrent Q-Network (DRQN) approaches are great options for the control of autonomous vehicles. DRQN algorithms are widely used in video game competitions, but not many studies are available for their use in autonomous vehicles. In this paper, we present a context-based learning approach using DRQN for driverless vehicles. Our experiments demonstrate the effectiveness of using the DRQN algorithm over others.
ISSN:2375-5261
DOI:10.1109/ISORC49007.2020.00033