With the advance of algorithms, deep reinforcement learning (DRL) offers solutions to
trajectory planning under uncertain environments. Different from traditional trajectory …

Recently, deep reinforcement learning (DRL)-based trajectory planning methods have been
designed for manipulator trajectory planning, given their potential in solving the problem of …

To improve the efficiency of deep reinforcement learning (DRL)-based methods for robotic
trajectory planning in the unstructured working environment with obstacles. Different from …

A new deep deterministic policy gradient (DDPG) integrating kinematics analysis and
immune optimization (KAI-DDPG) is proposed to address the drawbacks of DDPG in path …

Path planning is one of the key research topics in robotics. Nowadays, researchers pay
more attention to reinforcement learning (RL) and deep learning (DL) because of RL's good …

Deep reinforcement learning (DRL) is an essential technique for autonomous motion
planning of mobile robots in dynamic and uncertain environments. In attempting to acquire a …

Though there are extensive works on deep reinforcement learning (DRL) for robotics,
sequential trajectory generation for multiprocess robotic tasks based on DRL is yet to be …

We present a hierarchical deep reinforcement learning (DRL) framework with prominent
sampling efficiency and sim-to-real transfer ability for fast and safe navigation: the low-level …

Unmanned Aerial Vehicles (UAVs) can be an important component in the Internet of Things
(IoT) ecosystem due to their ability to collect and transmit data from remote and hard-to …

Deep reinforcement learning has the advantage of being able to encode fairly complex
behaviors by collecting and learning empirical information. In the current study, we have …