A systematic study on reinforcement learning based applications
We have analyzed 127 publications for this review paper, which discuss applications of
Reinforcement Learning (RL) in marketing, robotics, gaming, automated cars, natural …
Reinforcement Learning (RL) in marketing, robotics, gaming, automated cars, natural …
A review of the literature on fuzzy-logic approaches for collision-free path planning of manipulator robots
In recent years, a large number of manipulator robots have been deployed to replace or
assist humans in many repetitive and dangerous tasks. Yet, these robots have complex …
assist humans in many repetitive and dangerous tasks. Yet, these robots have complex …
Path planning based on deep reinforcement learning for autonomous underwater vehicles under ocean current disturbance
The path planning issue of the underactuated autonomous underwater vehicle (AUV) under
ocean current disturbance is studied in this paper. In order to improve the AUV's path …
ocean current disturbance is studied in this paper. In order to improve the AUV's path …
Mobile robot path planning using a QAPF learning algorithm for known and unknown environments
This paper presents the computation of feasible paths for mobile robots in known and
unknown environments using a QAPF learning algorithm. Q-learning is a reinforcement …
unknown environments using a QAPF learning algorithm. Q-learning is a reinforcement …
[PDF][PDF] Improved Dijkstra algorithm for mobile robot path planning and obstacle avoidance
Optimal path planning avoiding obstacles is among the most attractive applications of
mobile robots (MRs) in both research and education. In this paper, an optimal collision-free …
mobile robots (MRs) in both research and education. In this paper, an optimal collision-free …
Mobile robot path planning using fuzzy enhanced improved multi-objective particle swarm optimization (FIMOPSO)
This paper introduces a method for car-like mobile robot path planning (CRPP). The robot
works in both dynamic and static situations. The aim of this method is to explore the best …
works in both dynamic and static situations. The aim of this method is to explore the best …
A data-driven hybrid ensemble AI model for COVID-19 infection forecast using multiple neural networks and reinforced learning
W **, S Dong, C Yu, Q Luo - Computers in Biology and Medicine, 2022 - Elsevier
The COVID-19 outbreak poses a huge challenge to international public health. Reliable
forecast of the number of cases is of great significance to the planning of health resources …
forecast of the number of cases is of great significance to the planning of health resources …
Efficient path planning for mobile robot based on deep deterministic policy gradient
H Gong, P Wang, C Ni, N Cheng - Sensors, 2022 - mdpi.com
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 …
robot path planning, due to the limited observable environment of mobile robots, the training …
Improved ACO algorithm fused with improved Q-Learning algorithm for Bessel curve global path planning of search and rescue robots
W Fang, Z Liao, Y Bai - Robotics and Autonomous Systems, 2024 - Elsevier
Addressing issues with traditional ant colony and reinforcement learning algorithms, such as
low search efficiency and the tendency to produce insufficiently smooth paths that easily fall …
low search efficiency and the tendency to produce insufficiently smooth paths that easily fall …
A modified Q-learning path planning approach using distortion concept and optimization in dynamic environment for autonomous mobile robot
Autonomous mobile robot path planning in unknown and dynamic environment is a crucial
task for successful mobile robot navigation. This study proposes an improved Q-learning …
task for successful mobile robot navigation. This study proposes an improved Q-learning …