… the domain of autonomous driving expanding our conference … algorithms such imitation
learning, deep Q learning, among … or Deep RL applied to autonomous driving or the self driving …

… This is of particular relevance as it is difficult to pose autonomous driving as a supervised …
research for autonomous driving, we provide a short overview of deep reinforcement learning …

… In order 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 …

Tactical decision making for autonomous driving is challenging due to the diversity of … the
form of Monte Carlo tree search and deep reinforcement learning. The method is based on the …

… Gym, a Multi-Agent Connected, Autonomous Driving agent learning platform for … Autonomous
Driving (CAD) simulation environments that enable the research and development of Deep …

… model-free deep reinforcement learning in challenging urban autonomous driving scenarios.
… Several state-of-the-art model-free deep RL algorithms are implemented into our framework…

… deep reinforcement learning for autonomous driving to … deep reinforcement learning and
safety based control, including artificial potential field and path tracking for autonomous driving. …

… to implement it in car autonomy for human-level performance. In this paper, we present a
DRL-based autonomous driving strategy for urban environment using Deep Q Network. We use …

… It even gets worse as we attempt to tackle increasingly demanding and diverse problems in
autonomous driving, such as crossing an unsignalized intersection or doing an unprotected …

… Autonomous driving needs to deal with a variety of complex and … in autonomous driving
presents a broad application prospect. In this article, an end-to-end autonomous driving policy …