Reinforcement learning applications in dynamic pricing of retail markets
Contribuinte(s) |
Chung, JY Zhang, LJ |
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Data(s) |
27/06/2003
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Resumo |
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. |
Formato |
application/pdf |
Identificador |
http://eprints.iisc.ernet.in/43792/1/Reinforcement.pdf Raju, CVL and Narahari, Y and Ravikumar, K (2003) Reinforcement learning applications in dynamic pricing of retail markets. In: IEEE International Conference on E-Commerce (CEC 2003), 24-27 June 2003, Newport Beach, California. |
Publicador |
IEEE |
Relação |
http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1210269 http://eprints.iisc.ernet.in/43792/ |
Palavras-Chave | #Computer Science & Automation (Formerly, School of Automation) |
Tipo |
Conference Paper PeerReviewed |