… In this study, a deep reinforcement learning approach is proposed to handle tactical driving
… ground vehicles. Tactical driving is a challenging topic for unmanned ground vehicles …

… Thus, a hybrid architecture that combines a robust recursive regulator and a DRL algorithm
is presented in this paper to enhance performance of the machine learning method. During …

… vehicle orientation in positions within the range of the sensors. In this work, we present a
deep reinforcement learning approach … or local motion planning search spaces methods. Our …

… Deep Reinforcement Learning (DRL) is a novel method that combines DL and RL … of
continuous deep reinforcement learning for the autonomous navigation of four-wheeled vehicles in …

… Abstract—In this paper, we present a novel navigation system of unmanned ground vehicle
(UGV) for local path planning based on deep reinforcement learning. The navigation system …

… vehicles, we suppose that the maximum number of vehicles … range Rn of ground vehicles
for vehicle-to-vehicle (V2V) … other embedded wireless interfaces in vehicles, such that 0 < (…

… a reinforcement learning-based method (and in particular dyna-Q+) is presented for a team
of unmanned ground vehicles (… the proposed method for simultaneous learning and planning …

… , modern control methods, artificial intelligence, and machine learn… focused on Deep
Reinforcement Learning (DRL) approach. … used the ground truth information about the ego …

… vehicles using deep reinforcement in scenarios where human operators and traditional control
methods … has not yet been applied to wheeled ground vehicles in rough terrain with high …

… to-end model transfer trajectory planning method based on depth reinforcement learning is
proposed in this study. Furthermore, DDPG is a deep reinforcement learning method, and it is …