This paper proposes an improved Q-learning algorithm for the path planning of a mobile
robot in a free-space environment. Existing Q-learning methods for path planning focus on …

This paper deals with a new approach based on Q-learning for solving the problem of
mobile robot path planning in complex unknown static environments. As a computational …

As science and technology advance rapidly, the scope of mobile robot applications
continues to expand. In mobile robotics, path planning, which has formed a relatively …

Robotic systems which rapidly continue its development are increasingly used in our daily
life. Mobile robots both draw the map where they may move in their environment and reach …

This paper shows how Q-learning approach can be used in a successful way to deal with
the problem of mobile robot navigation. In real situations where a large number of obstacles …

Reinforcement learning (RL) is a learning technique based on trial and error. Q-learning is a
method of RL algorithms. It has been applied widely in the adaptive path planning for the …

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 …

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 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 …

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) …