… a deep reinforcement learning method to solve the decision-making and trajectory planning
problem of intelligent vehicles. The method employs a deep learning framework for feature …

… of the action space on the performance of trajectory planning policy, we present the training
curves of the optimization objectives of the proposed problem, as shown in Fig. 3 and Fig. 4. …

… to learn a continuous optimal trajectory planning policy via RL for the vehicle’s efficient
exploration. To this end, this section models the constrained minimum-time trajectory planning …

… This study employs an approximate deep reinforcement learning method to solve the navigation
problem in a dense environment with numerous static and moving obstacles. Our model …

… and trajectory planning algorithm for nonholonomic mobile robots that uses recent advances
in deep reinforcement learning… Our deep reinforcement learning agent not only processes a …

… Another part of research focuses on different layers of Motion Planning, such as strategic
decisions, trajectory planning, and control. A wide range of techniques in Machine …

… trajectory planning method with deep reinforcement learning in the federated learning scheme
(… -free automated parking trajectories in multiple narrow spaces. HALOES is a federated …

… learning perspective on hierarchical reinforcement learning, where the problem of
learning lower layers in a hierarchy is transformed into the problem of learning trajectory-level …

… Therefore, this paper proposes a segmented-updated lane-change trajectory planning …
entropy inverse reinforcement learning (IRL) algorithm for human demonstration learning and the …

… However, due to high computational demands, most of them are not a feasible solution
for real-time applications such as trajectory planning for quadrotors. For real-time planning, …