… 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 (…

… to bridge the gap between autonomous driving and reinforcement learning, we adopt the
deep deterministic policy gradient (DDPG) algorithm to train our agent in The Open Racing Car …

… (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 …

… 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 …

… Abstract— We present a control approach for autonomous vehicles based on deep
reinforcement learning. A neural network agent is trained to map its estimated state to acceleration …

… 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 …

… 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 …

… 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 …