… that investigates how the leading autonomous vehicles affect the urban network under a …
deep reinforcement learning agents. Secondly, we investigate the leading autonomous vehicle …

… deep reinforcement learning system for automated driving. The proposed framework leverages
merits of both rule-based and learning… rule based on common driving practice that ensure …

… Reinforcement learning is an umbrella term for a large number of algorithms derived for …
In our framework, the objective of reinforcement learning is to train a driving agent who can …

… process for monitoring of autonomous vehicles' dynamics system, … deep reinforcement
learning algorithm (NDRL) that can be used to maximize the robustness of autonomous vehicle …

… a novel deep reinforcement learning based decision-making structure for automated driving
at … A new training structure is proposed to improve the training performance of DQN, DDQN …

… deep reinforcement learning based CAV car … and automated vehicles (CAVs) longitudinal
control for a mixed connected and automated traffic environment based on deep reinforcement …

Deep reinforcement learning (DRL) is poised to … autonomous systems with a higher-level
understanding of the visual world. Currently, deep learning is enabling reinforcement learning (…

… learning-based method that combines deep reinforcement learning and imitation learning …
, which is applied to longitudinal vehicle motion control in autonomous driving scenarios. Our …

… can provide efficient solutions to the complex problems encountered in autonomous driving,
… safe deep reinforcement learning in the context of motion planning for autonomous driving. …

… The proposed deep deterministic policy gradient-based sequential … the autonomous vehicle
as a learning agent and trains it to drive on a lane, overtake a static and a moving vehicle, …