Applications of reinforcement learning in an open railway access market price negotiation

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 reinfor...

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Bibliographische Detailangaben

Hauptverfasser:	Shun King Wong, Chi Wai Tsang, Tin Kin Ho
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Costs Elasticity Learning machine learning Multiagent systems Pricing Problem-solving Rail transportation railway simulation reinforcement learning Resource management Software agents Tin
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