In this paper we propose an algorithm for multi-agent Q-learning. The algorithm is inspired by the natural behaviour of ants, which deposit pheromone in the environment to communicate. The benefit besides simulating ant behaviour in a colony is to design complex multi-agent systems. Complex behaviour can emerge from relatively simple interacting agents. The proposed Q-learning update equation includes a belief factor. The belief factor reflects the confidence the agent has in the pheromone detected in its environment. Agents communicate implicitly to co-ordinate and co-operate in learning to solve a problem.
CITATION STYLE
Monekosso, N., Remagnino, P., & Szarowicz, A. (2002). An improved Q-learning algorithm using synthetic pheromones. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2296, p. 197). Springer Verlag. https://doi.org/10.1007/3-540-45941-3_21
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