Technical Note: \cal Q-Learning
Machine Learning
Learning to act using real-time dynamic programming
Artificial Intelligence - Special volume on computational research on interaction and agency, part 1
Reinforcement learning for call admission control and routing in integrated service networks
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Machine Learning
Call Admission Control for Multimedia Cellular Networks Using Neuro-dynamic Programming
NETWORKING '02 Proceedings of the Second International IFIP-TC6 Networking Conference on Networking Technologies, Services, and Protocols; Performance of Computer and Communication Networks; and Mobile and Wireless Communications
On optimal call admission control in cellular networks
INFOCOM'96 Proceedings of the Fifteenth annual joint conference of the IEEE computer and communications societies conference on The conference on computer communications - Volume 1
Connection admission control for mobile multiple-class personal communications networks
IEEE Journal on Selected Areas in Communications
Robust dynamic admission control for unified cell and call QoS in statistical multiplexers
IEEE Journal on Selected Areas in Communications
Call admission control and routing in integrated services networks using neuro-dynamic programming
IEEE Journal on Selected Areas in Communications
IEEE Journal on Selected Areas in Communications
On the advantages of non-cooperative behavior in agent populations
Mathematics and Computers in Simulation
Constraint optimization in call admission control domain with a NeuroEvolution algorithm
Proceedings of the 3rd International Conference on Bio-Inspired Models of Network, Information and Computing Sytems
A call admission control scheme using neuroevolution algorithm in cellular networks
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A general framework for analyzing the optimal call admission control in DS-CDMA cellular network
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part II
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In this paper, we address the call admission control (CAC) problem in a cellular network that handles several classes of traffic with different resource requirements. The problem is formulated as a semi-Markov decision process (SMDP) problem. We use a real-time reinforcement learning (RL) [neuro-dynamic programming (NDP)] algorithm to construct a dynamic call admission control policy. We show that the policies obtained using our TQ-CAC and NQ-CAC algorithms, which are two different implementations of the RL algorithm, provide a good solution and are able to earn significantly higher revenues than classical solutions such as guard channel. A large number of experiments illustrates the robustness of our policies and shows how they improve quality of service (QoS) and reduce call-blocking probabilities of handoff calls even with variable traffic conditions.