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 …

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

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 …

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) …

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 …

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 …

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 …

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 …

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 …