Route optimization with Q-learning

Authors:
Semiye Demircan;Musa Aydin;S. Savas Durduran
Affiliations:
Computer Engineering Department, Selcuk University, Konya, Turkiye;Electrics and Electronics Department, Selcuk University, Konya, Turkiye;Geodesy and Photogrammetry Engineering Department, Selcuk University, Konya, Turkiye
Venue:
ACS'08 Proceedings of the 8th conference on Applied computer scince
Year:
2008

Citing 3
Cited 1

Multiagent systems: a modern approach to distributed artificial intelligence

Multiagent systems: a modern approach to distributed artificial intelligence
Machine Learning

Machine Learning
Reinforcement learning: a survey

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Modeling of route planning system based on Q value-based dynamic programming with multi-agent reinforcement learning algorithms

Engineering Applications of Artificial Intelligence

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Abstract

Due to increasing energy requirement the consideration of route determination is becoming important. The aim of this project is to find optimum result considering its important criteria. In this work, Geographic Information System (GIS) based energy transmission route optimization had been performed. In this optimization, using Multiagent Systems which is subdirectory of Distributed Artificial Intelligence the criteria affecting energy transmission line had been severally analyzed. The application had been actualized on the Selcuk University Campus Area. Therefore the digital map of the campus area particularly had been composed containing of relevant criteria. Using Q- learning Algorithm of Multiagent System the optimum route had been determined.