How to generate the path planning of mobile robots quickly is a problem in the field of
robotics. The Q-learning (QL) algorithm has recently become increasingly used in the field of …

Path planning is a major challenging problem for mobile robots, as the robot is required to
reach the target position from the starting position while simultaneously avoiding conflicts …

In fact, optimizing path within short computation time still remains a major challenge for
mobile robotics applications. In path planning and obstacles avoidance, Q-Learning (QL) …

Q-learning is a classical reinforcement learning algorithm and one of the most important
methods of mobile robot path planning without a prior environmental model. Nevertheless, Q …

The path‐planning approach plays an important role in determining how long the mobile
robots can travel. To solve the path‐planning problem of mobile robots in an unknown …

In order to solve the problems in the optimum path planning of autonomous mobile robot in
unknown complex environment, such as slow convergence rate and contact collision with …

To address the issues of slow convergence speed and poor path planning performance in
dynamic obstacle environments. This paper proposes an improved Q-Learning path …

The Q-Learning algorithm is a reinforcement learning technique widely used in various
fields such as path planning, intelligent transportation, penetration testing, among others. It …

In order to realize the optimization of autonomous navigation of mobile robot under the
condition of partial environmental knowledge known. An improved Q-learning reinforcement …

Path planning of a mobile robot under dynamic environment is a difficult part of robot
navigation. In this paper, a new path planning method based on improved Q-learning (IQL) …