… This dissertation argues that reinforcement learning utilizing stored instances of past …
systems such as autonomous vehicles. I present the results of the learning algorithm evaluated on …

… research for autonomous driving, we provide a short overview of deep reinforcement learning
and then … in a scenario of complex road curvatures and simple interaction of other vehicles. …

… that needs to be considered for autonomous vehicles (AVs). To … reinforcement learning
to find a risk-aware driving decision strategy with the minimum expected risk for autonomous …

… , thus, to propose a novel adversarial deep reinforcement learning (RL) framework that aims
… from intravehicle sensors such as camera, radar, RSSs, and intervehicle beaconing. We …

… stability guarantee for autonomous vehicles with limited … learning-based algorithm with the
stability guarantee is proposed to train policies for the motion planning of autonomous vehicles…

… of self-driving vehicles using the deep reinforcement learning model, in which learning
with an experience of professional driver and a Q-learning algorithm with filtered experience …

… the vehicles… vehicles. A Composite Platoon Trajectory Planning Strategy (CPTPS) proposed
in [25], develops a trajectory plan for the leader vehicles and applies reinforcement learning …

… reinforcement learning approach for robust lane change decision making of autonomous
vehicles… decision making behaviors of autonomous vehicles under policy constraints and …

Autonomous decision making is a critical and difficult task for intelligent vehicles in dynamic
transportation environments. In this paper, a reinforcement learning approach with value …

… for verifying and validating the autonomous driving systems running in traffic with … vehicle
interactions based on deep reinforcement learning (DRL) and game theory (GT), since learning …