… deep Q-network. The Q-network is trained by using a customized variant of … deep double
Qlearning algorithm from [7], see Algorithm 1. For decision making of an AV in a highway driving …

… Abstract: Autonomous driving has been the trend. In this paper, a Deep Reinforcement
Learning (… vehicles on highway driving. To avoid the overestimate action values induced by Q-…

… deep reinforcement learning system for automated driving. The proposed framework leverages
merits of both rule-based and learning… rule based on common driving practice that ensure …

… road curvatures and simple interaction of other vehicles. … Deep Reinforcement Learning(DRL),
we propose a pipelined framework for end-end training of a DNN for autonomous driving …

… traffic accidents and improve driving efficiency. In this work, a deep reinforcement learning
(DRL)-… for autonomous vehicles to address the overtaking behaviors on the highway. First, a …

… in a real-world highway dataset. On the other hand, Bojarski et al. [3] achieve autonomous
… between autonomous driving and reinforcement learning, we adopt the deep deterministic …

… deep reinforcement learning (DRL) algorithms and provides a taxonomy of automated driving
tasks … policies for complex navigation tasks, scenario-based policy learning for highways, …

… 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 …

… a simpler task first and then moving to learning a more complex task. … training the agent is
similar to how humans learn to drive. We are first taught to drive a car straight on an empty road…

… Deep Reinforcement Learning (DRL). Our DRL approach considers multiple objectives,
including safety, passengers’ comfort, and efficient road … real-world driving conditions. Notably, …