… (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 …

… 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 …

… 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 …

… On the other hand, with reinforcement learning (RL), a policy can be learned and improved
… model-free deep reinforcement learning in challenging urban autonomous driving scenarios. …

… 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 …

… of DRL in various autonomous driving (AD) applications. We first provide an overview of the
tasks in autonomous driving systems, reinforcement learning algorithms and applications of …

… In order to improve the data efficiency, we propose visual TORCS (VTORCS), a deep
reinforcement learning environment which is based on the open racing car simulator (TORCS). By …

… of interactions with the environment to learn a functioning policy. It … in autonomous driving,
such as crossing an unsignalized intersection or doing an unprotected left turn in dense traffic. …

… machine learning framework, Deep Reinforcement Learning, … With this approach, it is of vital
importance to learn the … framework based on Deep Reinforcement Learning techniques for …

… paper, or any other machine learning technique, it is important to note that the agent will only
be able to solve the type of situations it encounters during the training process. Therefore, it …