Abstract
Despite great advances in controlling vehicles for autonomous driving and in deep reinforcement learning (DRL) techniques, designing an end-to-end architecture that supports autonomous driving using DRL techniques while facing uncertainties in complex and dynamic environments still remains challenging. By examining the state-of-the-art works in the domain of DRL for autonomous driving and inspired from the work of [1], we have designed an end-to-end autonomous driving system using the Ape-X algorithm [2] in Carla simulation environment [3] and have evaluated the performance by comparing its results to those that are obtained using other DRL techniques.
Published Version
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