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Asymmetric multiagent reinforcement learning in pricing applications

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2 Author(s)
Könönen, V. ; Neural Networks Res. Centre, Helsinki Univ. of Technol., Espoo, Finland ; Oja, E.

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.

Published in:

Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on  (Volume:2 )

Date of Conference:

25-29 July 2004