@inproceedings{d9ee11ba2d104459a0f7d51c3e1bf41a,
title = "Reinforcement learning for dynamic channel allocation in cellular telephone systems",
abstract = "In cellular telephone systems, an important problem is to dynamically allocate the communication resource (channels) so as to maximize service in a stochastic caller environment. This problem is naturally formulated as a dynamic programming problem and we use a reinforcement learning (RL) method to find dynamic channel allocation policies that are better than previous heuristic solutions. The policies obtained perform well for a broad variety of call traffic patterns. We present results on a large cellular system with approximately 4949 states.",
author = "Satinder Singh and Dimitri Bertsekas",
year = "1997",
language = "English (US)",
isbn = "0262100657",
series = "Advances in Neural Information Processing Systems",
publisher = "Neural information processing systems foundation",
pages = "974--980",
booktitle = "Advances in Neural Information Processing Systems 9 - Proceedings of the 1996 Conference, NIPS 1996",
note = "10th Annual Conference on Neural Information Processing Systems, NIPS 1996 ; Conference date: 02-12-1996 Through 05-12-1996",
}