The goal of this paper is to improve the performance of the well known Q learning algorithm,
the robust technique of Machine learning to facilitate path planning in an environment. Until …

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) …

Mobile robot path planning is a key technology in the field of robotic research and
applications. Q-learning algorithm is one of the most effective methods to solve the problem …

Classical Q-learning requires huge computations to attain convergence and a large storage
to save the Q-values for all possible actions in a given state. This paper proposes an …

Q Learning is a form of reinforcement learning for path finding problems that does not
require a model of the environment. It allows the agent to explore the given environment and …

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 …

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 …

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 …

Path planning is an essential algorithm in autonomous mobile robots, including agricultural
robots, to find the shortest path and to avoid collisions with obstacles. Q-Learning algorithm …

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 …