Classical Q-learning takes huge computation to calculate the Q-value for all possible actions
in a particular state and takes large space to store its Q-value for all actions, as a result of …

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 …

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 …

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) …

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 …

Path planning in an environment with obstacles is an ongoing problem for mobile robots. Q-
learning algorithm increases its importance due to its utility in interacting with the …

Path planning is a fundamental method in solving mazes or moving robots traversing
through open fields with obstacles. Q-learning method is a model-independent …

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 proposed a novel approach to determine the optimal trajectory of the path for
multi-robots in a clutter environment using hybridization of improved particle swarm …

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 …