DocumentCode :
3640096
Title :
Combining Rule Induction and Reinforcement Learning: An Agent-based Vehicle Routing
Author :
Bartlomiej Sniezynski;Wojciech Wojcik;Jan D. Gehrke;Janusz Wojtusiak
Author_Institution :
Dept. of Comput. Sci., AGH Univ. of Sci. &
fYear :
2010
Firstpage :
851
Lastpage :
856
Abstract :
Reinforcement learning suffers from inefficiency when the number of potential solutions to be searched is large. This paper describes a method of improving reinforcement learning by applying rule induction in multi-agent systems. Knowledge captured by learned rules is used to reduce search space in reinforcement learning, allowing it to shorten learning time. The method is particularly suitable for agents operating in dynamically changing environments, in which fast response to changes is required. The method has been tested in transportation logistics domain in which agents represent vehicles being routed in a simple road network. Experimental results indicate that in this domain the method performs better than traditional Q-learning, as indicated by statistical comparison.
Keywords :
"Learning","Roads","Multiagent systems","Plasmas","Computational modeling","Logistics","Vehicles"
Publisher :
ieee
Conference_Titel :
Machine Learning and Applications (ICMLA), 2010 Ninth International Conference on
Print_ISBN :
978-1-4244-9211-4
Type :
conf
DOI :
10.1109/ICMLA.2010.132
Filename :
5708955
Link To Document :
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