… Specifically, using deep reinforcement learning, this work develops a time-efficient navigation
policy that respects common social norms. The proposed method is shown to enable fully …

Robots that navigate among pedestrians use collision avoidance algorithms to enable safe
and efficient operation. Recent works present deep reinforcement learning as a framework to …

… , such as strategic decisions, trajectory planning, and control. A wide range of … , Deep
Reinforcement Learning (DRL). The paper provides insight into the hierarchical motion planning …

… Abstract—This paper introduces a hybrid algorithm of deep reinforcement learning (RL) and
Force-based motion planning (FMP) to solve distributed motion planning problem in dense …

… In conclusion, the centralized reinforcement learning motion planning system has strong
coordination ability, and does not need to consider the cooperation between robots. …

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

… Abstract—In this survey, we systematically summarize the current literature on studies that
apply reinforcement learning (RL) to the motion planning and control of autonomous vehicles. …

… deep reinforcement learning (DRL) [5]. DRL automates the process by mapping high-dimensional
sensory information to robot motion … actor–critic deep reinforcement learning (DRL) …

… In this letter, we consider motion planning under LTL task specifications in continuous
state … An unsupervised one-shot and on-thefly DDPG-based motion planning framework is …

… In this article, a general motion planning framework that integrates deep reinforcement
learning (DRL) is proposed to explore the length-optimal path in Cartesian space and to derive …