… Deep Reinforcement Learning(DRL), we propose a pipelined framework for end-end training
of a DNN for autonomous driving … , and the output is the driving actions. We discuss each …

… , Artificial Intelligence and Machine Learning methods are … in Machine Learning itself have
been developed, and this article describes one of these fields, Deep Reinforcement Learning (…

… (RL) has become a powerful learning framework now capable of learning complex policies
… deep reinforcement learning (DRL) algorithms and provides a taxonomy of automated driving …

… traffic accidents and improve driving efficiency. In this work, a deep reinforcement learning
(DRL)-enabled decision-making policy is constructed for autonomous vehicles to address the …

… Abstract—Deep Reinforcement Learning has led us to newer … Deep Reinforcement Learning
autonomous navigation and obstacle avoidance of self-driving cars, applied with Deep Q …

… We explore the effectiveness of Deep Reinforcement Learning to handle intersection
problems. Using recent advances in Deep RL, we are able to learn policies that surpass the …

… Our approach aims to develop an agent that generates autonomous vehicle control based
on Deep Reinforcement Learning algorithm that solves a MDP. The following sections show …

… In this work, the problem of path planning for an autonomous vehicle that moves on a … of a
driving policy based on reinforcement learning. In this way, the proposed driving policy makes …

… However, the traditional machine learning… Deep Reinforcement learning and Genetic
algorithm for Smart-Platooning (DRG-SP) the AVs. The leverage of the deep reinforcement learning …

… more diverse driving styles. The desired, expert-like driving behavior of the autonomous
vehicle is … MDP and determine the optimal driving strategy for the autonomous vehicle using …