Abstract
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.
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Monekosso, N., Remagnino, P., Szarowicz, A. (2002). An Improved Q-Learning Algorithm Using Synthetic Pheromones. In: Dunin-Keplicz, B., Nawarecki, E. (eds) From Theory to Practice in Multi-Agent Systems. CEEMAS 2001. Lecture Notes in Computer Science(), vol 2296. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45941-3_21
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DOI: https://doi.org/10.1007/3-540-45941-3_21
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