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

… planning policies directly determine how AVs behave. In this paper, we model the trajectory
planning … process (POMDP), and develop a reinforcement learning (RL)-based approach to …

… This article presents a technique for trajectory planning based on parameterized high-level
actions. These high-level actions are subtrajectories that have variable shape and duration. …

… In this section, we show that DDPG and SAC can learn optimal trajectory planning for
dynamic obstacles collision avoidance. For the evaluation, we compare two different DRL …

… The DDPG algorithm is a combination of deep learning and reinforcement learning. Based
on this algorithm, this paper designs an autonomous path planning model for unmanned …

… Following decision-making, the trajectory planning stage is entered into, where its output is
… the unified trajectory planning model in Section IV to generate a new trajectory path together …

… Motivated by the electric attraction and electrostatic repulsion between charges, we model
the orientation reward function for trajectory planning according to Coulomb’s law [29] in this …

… Reinforcement Learning (HRL) framework for learning autonomous driving policies. We adapt
a state-ofthe-art algorithm, Hierarchical Double Deep Q-learning (h… waypoint trajectories to …

… train and test the model. The proposed model can quickly converge in training phase. Testing
… dynamic lane-changing trajectory planning model, our model can reduce collision risk. It is …

… ground IoT devices in the backward path. We adopt reinforcement learning for assisting
UAVs to learn collision avoidance without knowing the trajectories of other UAVs in advance. In …