Existing mobile robots cannot complete some functions. To solve these problems, which
include autonomous learning in path planning, the slow convergence of path planning, and …

Deep reinforcement learning (RL) agents are able to learn contact-rich manipulation tasks
by maximizing a reward signal, but require large amounts of experience, especially in …

When a traditional Deep Deterministic Policy Gradient (DDPG) algorithm is used in mobile
robot path planning, due to the limited observable environment of mobile robots, the training …

We present PRM-RL, a hierarchical method for long-range navigation task completion that
combines sampling-based path planning with reinforcement learning (RL). The RL agents …

Motion planning and its optimization is vital and difficult for redundant robot manipulator in
an environment with obstacles. In this article, a general motion planning framework that …

Although deep reinforcement learning (DRL) algorithms with experience replay have been
used to solve many sequential learning problems, applications of DRL in real-world robotics …

The focus of this work is to enumerate the various approaches and algorithms that center
around application of reinforcement learning in robotic ma-]] nipulation tasks. Earlier …

Motion planning for high degree-of-freedom (DOF) robots is challenging, especially when
acting in complex environments under sensing uncertainty. While there is significant work on …

Collision-free motion is essential for mobile robots. Most approaches to collision-free and
efficient navigation with wheeled robots require parameter tuning by experts to obtain good …

In order to achieve collision-free path planning in complex environment, Munchausen deep
Q-learning network (M-DQN) is applied to mobile robot to learn the best decision. On the …