A New Approach in Agent Path-Finding using State Mark Gradients

Since searching is one of the most important problem-solving methods, especially in Artificial Intelligence where it is often difficult to devise straightforward solutions, it has been given continuous attention by researchers. In this paper a new algorithm for agent path-finding is presented. Our approach is based on environment marking during exploration. We tested the performances of Q-learning and Learning Real-Time A* algorithm for three proposed mazes and then a comparison was made between our algorithm, two variants of Q-learning and LRTA* algorithm.