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Abstract: We study novel aspects of multi-agent Qlearning in a model market in which two identical, competing "pricebots" strategically price a commodity. Two fundamentally different solutions are observed: an exact, stationary solution with zero Bellman error consisting of symmetric policies, and a non-stationary, broken-symmetry pseudosolution, with small but non-zero Bellman error. This "pseudo-convergent" asymmetric solution has no analog in ordinary Qlearning. We calculate analytically... (Update)
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BibTeX entry: (Update)
J.O. Kephart and G.J. Tesauro, "Pseudo-Convergent Q-Learning by Competitive Pricebots," Proc. 17th Int'l Conf. Machine Learning, Morgan Kaufmann, 2000, pp. 463-470. http://citeseer.ist.psu.edu/kephart00pseudoconvergent.html More
@inproceedings{ kephart00pseudoconvergent,
author = "Jeffrey O. Kephart and Gerald J. Tesauro",
title = "Pseudo-convergent {Q}-Learning by Competitive Pricebots",
booktitle = "Proc. 17th International Conf. on Machine Learning",
publisher = "Morgan Kaufmann, San Francisco, CA",
pages = "463--470",
year = "2000",
url = "citeseer.ist.psu.edu/kephart00pseudoconvergent.html" }
Citations (may not include all citations):51 Shopbots and pricebots - Greenwald, Kephart - 1999
1 Workshop on Adaptation and Learning in Multiagent Systems (context) - Sandholm, Crites et al. - 1995
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