… to the deep Q-network. The Q-network is trained by using a customized variant of the deep
double Q… For decision making of an AV in a highway driving scenario, we consider four action …

… Abstract: Autonomous driving has been the trend. In this paper, a Deep Reinforcement …
making and interaction between vehicles on highway driving. To avoid the overestimate action …

… deep reinforcement learning system for automated driving. … safety rule based on common
driving practice that ensure a … that learns safety patterns from driving data. Specifically, the …

… We propose a Deep Reinforcement Learning-based approach in this work for merging
decisions of AV with dynamic constraints and reasoning in moderate traffic through highway …

… This following organization of this article is given as follows: the highway driving environment
and the control modules of the ego and surrounding vehicles are described in Section II. …

… [21] present a framework to learn costmaps for autonomous driving using IRL directly from
sensor data. [22] present an IRL approach in a simple highway driving scenario on a custom …

… , we focus on Deep Reinforcement Learning (DRL), which incorporates deep neural networks
in … We have divided our scenarios into two distinct traffic conditions: highway and urban. In …

… The fully automated highway driving (AHD) has been researched by the control engineers
… Chan, “Formulation of deep reinforcement learning architecture toward autonomous driving …

… for self-driving cars in a multi-lane, single-agent setting. The proposed approach utilizes
deep reinforcement … Simulations from a wellknown self-driving car simulator demonstrate the …

… deep reinforcement learning (DRL) algorithms and provides a taxonomy of automated driving
tasks … in real world deployment of autonomous driving agents. It also delineates adjacent …