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Dynamic pricing based on asymmetric multiagent reinforcement learning

✍ Scribed by Ville Könönen

Publisher
John Wiley and Sons
Year
2005
Tongue
English
Weight
350 KB
Volume
21
Category
Article
ISSN
0884-8173

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✦ Synopsis

A dynamic pricing problem is solved by using asymmetric multiagent reinforcement learning in this article. In the problem, there are two competing brokers that sell identical products to customers and compete on the basis of price. We model this dynamic pricing problem as a Markov game and solve it by using two different learning methods. The first method utilizes modified gradient descent in the parameter space of the value function approximator and the second method uses a direct gradient of the parameterized policy function. We present a brief literature survey of pricing models based on multiagent reinforcement learning, introduce the basic concepts of Markov games, and solve the problem by using proposed methods.

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