… algorithms to enable safe and efficient operation. Recent works present deep reinforcement
learning as a … This work extends our previous approach to develop an algorithm that learns …

… on studies that apply reinforcement learning (RL) to the motion planning and control of …
Finally, the remaining challenges applying deep RL algorithms on autonomous driving are …

… obtain the ability for semantic description and abstract reasoning representation, will be an
important research direction in the field of motion planning for deep reinforcement learning. …

… A wide range of techniques in Machine Learning itself have been developed, and … Deep
Reinforcement Learning (DRL). The paper provides insight into the hierarchical motion planning …

… algorithm of deep reinforcement learning (RL) and Force-based motion planning (FMP) to
solve distributed motion planning … Individually, RL and FMP algorithms each have their own …

… 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 …

… Principles of typical motion planning algorithms are investigated … These algorithms include
traditional planning algorithms, … motion planning algorithms with advanced machine learning (…

… Science Department Machine Learning and Data … motion and trajectory planning algorithm
for nonholonomic mobile robots that uses recent advances in deep reinforcement learning. …

… This work addresses the above-motioned issues by proposing an onboard actor–critic
deep reinforcement learning (DRL) approach that allows safe goal navigation by mapping …

… Therefore, the deep learning DRL algorithm is extremely important … path planning[7]. In this
paper, the development, application and prospect of deep reinforcement learning algorithms …