Context-based learning for autonomous vehicles
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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.
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