… Learning itself have been developed, and this article describes one of these fields, Deep
Reinforcement Learning (DRL… The paper describes vehicle models, simulation possibilities and …

… of the autonomous vehicle is obtained as … autonomous vehicle using reinforcement learning
techniques. Second, we collect a number of demonstrations from an expert driver and learn …

… Reinforcement Learning to handle intersection problems. Using recent advances in Deep
RL, we are able to learn … We then explore a system’s ability to learn active sensing behaviors …

… and deployment of autonomous vehicles is the … autonomous vehicles in an accelerated
mode, without loss of unbiasedness. From naturalistic driving data, the background agents learn …

… We train in simulation and verify the learned policy on an autonomous vehicle. We
collected data from an autonomous vehicle in Mountain View, California, at an unsigned Tjunction, …

… autonomous cars could then monopolize the traffic. Using multi-agent reinforcement learning
(MARL) algorithms, researchers have attempted to design autonomous vehicles for both …

… all fields of technology, and Autonomous Vehicles (AV) research is one … Learning (DL) in
the control layer of an autonomous vehicle. More specifically, Deep Reinforcement Learning (…

… 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 Autonomous driving is a promising technology to reduce traffic … deep reinforcement
learning (DRL)-enabled decision-making policy is constructed for autonomous vehicles …

… of an autonomous vehicle and use RL to build a decision-making system for autonomous …
systems, data-driven learning control methods, especially reinforcement learning (RL), have …