Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/38051
Title: Applications of reinforcement learning in an open railway access market price negotiation
Authors: Wong, SK
Tsang, CW
Ho, TK
Keywords: Machine learning
Railway simulation
Reinforcement learning
Issue Date: 2008
Source: IEEE International Conference on Systems, Man and Cybernetics, 2008. SMC 2008. Singapore, 14-16 October 2008, p. 2309-2314 (CD) How to cite?
Abstract: In an open railway access market price negotiation, it is feasible to achieve higher cost recovery by applying the principles of price discrimination. The price negotiation can be modeled as an optimization problem of revenue intake. In this paper, we present the pricing negotiation based on reinforcement learning model. A negotiated-price setting technique based on agent learning is introduced, and the feasible applications of the proposed method for open railway access market simulation are discussed.
URI: http://hdl.handle.net/10397/38051
ISBN: 978-1-4244-2383-5
978-1-4244-2384-2 (E-ISBN)
DOI: 10.1109/ICSMC.2008.4811637
Appears in Collections:Conference Paper

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