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, a type of reinforcement learning, has gained increasing popularity in
autonomous mobile robot path planning recently, due to its self-learning ability without …

Path planning is an essential element in mobile robot navigation. One of the popular path
planners is Q-learning–a type of reinforcement learning that learns with little or no prior …

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 …

In order to solve the problem of slow convergence speed and long planned path when the
robot plans a path in unknown environment by using Q-learning algorithm, we propose the …

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 …

This paper provides a new deterministic Q-learning with a presumed knowledge about the
distance from the current state to both the next state and the goal. This knowledge is …

In this paper, a new approach is developed for solving the problem of mobile robot path
planning in an unknown dynamic environment based on Q-learning. Q-learning algorithms …

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) …

For a swarm of mobile robots in an unknown environment, the most challenging task is to
plan the optimal path and also to learn the environmental parameters. Machine learning is …