… (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 …

… , 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 (…

… only deal with simple driving tasks like lane keeping. In this article, we propose an interpretable
deep reinforcement learning method for end-to-end autonomous driving, which is able to …

… In this paper, deep reinforcement learning algorithms combining with risk assessment
functions are innovatively proposed to find an optimal driving strategy with the minimum expected …

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

… deep reinforcement learning framework to solve the taxi dispatch problem. The framework
can be used to redistribute vehicles … policy-based deep reinforcement learning algorithm as a …

… deep reinforcement learning (RL) based traffic control applications are surveyed. Specifically,
traffic signal control (TSC) applications based on (deep) … In addition to autonomous driving, …

… The results indicate that the proposed approach could contribute to the development of
better autonomous driving systems. Source code of this paper can be found at https://github.com/…

… deep reinforcement learning … deep learning technologies used in autonomous driving, and
provide a survey on state-of-the-art deep learning and AI methods applied to self-driving cars. …

… to re-render a scene from a different viewpoint, which could be useful for laying new learning
environments for Reinforcement Learning methods, and ultimately producing more gen…