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Asymmetric multiagent reinforcement learning in pricing applications | IEEE Conference Publication | IEEE Xplore

Asymmetric multiagent reinforcement learning in pricing applications


Abstract:

Two pricing problems are solved by using asymmetric multiagent reinforcement learning methods in this paper. In the first problem, a flat pricing scenario, there are two ...Show More

Abstract:

Two pricing problems are solved by using asymmetric multiagent reinforcement learning methods in this paper. In the first problem, a flat pricing scenario, there are two competing brokers that sell identical products to customers and compete on the basis of price. The second problem is a hierarchical pricing scenario, where a supplier sells products to two competing brokers. In both cases, the methods converged and led to very promising results. We present a brief literature survey of pricing models based on reinforcement learning, introduce the basic concepts of Markov games and solve two pricing problems based on multiagent reinforcement learning.
Date of Conference: 25-29 July 2004
Date Added to IEEE Xplore: 17 January 2005
Print ISBN:0-7803-8359-1
Print ISSN: 1098-7576
Conference Location: Budapest, Hungary

References

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