… changeable traffic scenarios, so the application of DRL in autonomous driving presents a
broad application prospect. In this article, an end-to-end autonomous driving policy learning …

… Autonomous Vehicles have become a highly popular … , vehicle control, social behavior,
and communication. Our survey focuses solely on deep learning and reinforcement learning …

… deep reinforcement learning, we can efficiently tackle continuous control problems. But the
deep reinforcement learning … ) with deep reinforcement learning for autonomous driving to put …

… (AI) and Machine Learning (ML) techniques to the development of autonomous driving systems
is … In Japan, the government has set aside enormous funds to make autonomous driving …

… of deep reinforcement learning (RL). We … of machine learning, deep learning and reinforcement
learning. Next we discuss core RL elements, including value function, in particular, Deep …

… authors have already attempted to address the notion of safe learning in this context (eg [3,2,…
an autonomous driving software stack that has already been used to drive autonomously for …

… as robotics and video games, the use of deep reinforcement learning (DRL) and deep imitation
… The earliest autonomous vehicles can be dated back to [5]–[7]. One milestone was the …

… autonomous driving systems that are scalable beyond geo-fenced operational design domains.
Deep Reinforcement Learning (RL) … for developing adaptive learning based solutions. …

… We provide the first example of training a self-driving vehicle using reinforcement learning …
RL training, it is possible to obtain a self driving vehicle that can be placed in the real world. …

… Deep Sets for deep reinforcement learning as a flexible and permutation invariant architecture
to handle a variable number of surrounding vehicles … size in reinforcement learning. Our …