Abstract
Rationality and convergence are two important criterions for multi-agent learning. A novel method called Pareto-Q learning is prompted for cooperative general-sum games, with the Pareto Optimum allowing rationality and social conventions benefiting the convergence. Experiments with the grid game suggest the efficiency of Pareto-Q. Compared with the single-agent Q-learning and Nash agent Q-learning, Pareto-Q learning performs best.
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© 2005 Springer-Verlag Berlin Heidelberg
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Song, M., Gu, G., Zhang, G. (2005). Pareto-Q Learning Algorithm for Cooperative Agents in General-Sum Games. In: Pěchouček, M., Petta, P., Varga, L.Z. (eds) Multi-Agent Systems and Applications IV. CEEMAS 2005. Lecture Notes in Computer Science(), vol 3690. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11559221_64
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DOI: https://doi.org/10.1007/11559221_64
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