Florin Leon, Mihai Horia Zaharia, Dan Galea
Artificial intelligence, path-finding, maze, reinforcement learning, Q-learning, LRTA*, agents.
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.
Department of Automatic Control and Computer Engineering,
Technical University "Gh. Asachi",
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