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Application of reinforcement learning in dynamic pricing algorithms

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2 Author(s)
Wang Jintian ; Dept. of Comput. Sci. & Technol., Hefei Univ. of Technol., Hefei, China ; Zhou Lei

This paper is concerned with the dynamic pricing problems of a duopoly case in electronic retail markets. Combined with the concept of performance potential, the simulated annealing Q-learning (SA-Q) and the win-or-learn-fast policy hill climbing algorithm (WoLF-PHC) are used to solve the learning problems of multi-agent systems with either average- or discounted-reward criteria, under the case that only partial information about the opponent is known. The simulation results show that the WoLF-PHC algorithm performs well in adapting environment's change and in deriving better learning values than the SA-Q algorithm.

Published in:

Automation and Logistics, 2009. ICAL '09. IEEE International Conference on

Date of Conference:

5-7 Aug. 2009