… We formulate the time-optimal trajectory planning problem in the reinforcement learning
framework. To this end, we model the task using an infinite-horizon Markov Decision Process (…

… reinforcement learning process. In this study, we propose an intelligent vehicle trajectory
planning method based on reinforcement learning … for vehicle trajectory planning that integrates …

… trajectories from any start position to generate high-quality training data. In the following
Section 3.1, we show how the path planning … DRL algorithms directly to learn the optimal paths. …

… of deep reinforcement learning (DRL)-based methods for robot manipulator trajectory planning
in … to speed up the learning process with a more reasonable trajectory by modeling the …

… However, this may result in conservative planning and … We propose a reinforcement
learning (RL) based solution to … motion planning problem, we search for the best trajectory in …

… planning can collaboratively learn from each other to overcome their own drawbacks. In “CO-PILOT”,
a learnable path-planner … [25] to recursively divide a trajectory from small T to large …

… a cooperative trajectory planning algorithm based on MCTS [9], and Maximum Entropy Inverse
Reinforcement Learning [6], yielding a system that is similar to Guided Cost Learning [13]. …

… reinforcement learning (DRL) to decide the control actions of multiple UAVs in order to
achieve accurate multitarget tracking. Reinforcement learning (RL) is a type of machine learning …

… is instead given by Reinforcement Learning (RL) approaches. Specifically, Deep … planning
trajectories in the joint space is simpler and computationally lighter, planning trajectories in …

… A number of systems for planning KN interventions have been proposed to assist the … the
planning process. The main idea of these studies is to describe curvilinear trajectory planning …