… 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. …

… to tackle complex trajectory planning under uncertain environments. However, deep RL still
… In this article, collision-free trajectory planning under uncertain environments is tackled with …

… trajectory planning. The primary contributions of this paper are summarized as follows: 1)
Considering the features of trajectory planning … of DRL methods in trajectory planning task. 2) …

… the problem of the UAV trajectory planning for the UAV-WSN … ’s trajectory and selecting CHs
in predetermined clusters of the ground WSN. 2) We show that the UAV’s trajectory planning …

… agent deep reinforcement learning based solution, with the help of the popular Multi-Agent
Deep … Thus, in this article, a Multi-Agent deep reinforcement learning based Trajectory control …

… A flight trajectory planning optimization is formulated as a Partial Observable Markov … an
onboard deep reinforcement learning algorithm to optimize the realtime trajectory planning of …

… 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 …

… -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 …

This article is concerned with the problem of planning optimal maneuver trajectories and
guiding the mobile robot toward target positions in uncertain environments for exploration …

… ABSTRACTDeep reinforcement learning (DRL) has been … This paper proposes a DRL-based
trajectory planner for … A simulation study is conducted to investigate the trajectory planning …