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Reinforcement learning applications in dynamic pricing of retail markets

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3 Author(s)
Raju, C.V.L. ; Dept. of Comput. Sci. & Autom., Indian Inst. of Sci., Bangalore, India ; Narahari, Y. ; Ravikumar, K.

In this paper, we investigate the use of reinforcement learning (RL) techniques to the problem of determining dynamic prices in an electronic retail market. As representative models, we consider a single seller market and a two seller market, and formulate the dynamic pricing problem in a setting that easily generalizes to markets with more than two sellers. We first formulate the single seller dynamic pricing problem in the RL framework and solve the problem using the Q-learning algorithm through simulation. Next we model the two seller dynamic pricing problem as a Markovian game and formulate the problem in the RL framework. We solve this problem using actor-critic algorithms through simulation. We believe our approach to solving these problems is a promising way of setting dynamic prices in multi-agent environments. We illustrate the methodology with two illustrative examples of typical retail markets.

Published in:

E-Commerce, 2003. CEC 2003. IEEE International Conference on

Date of Conference:

24-27 June 2003

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