(Enter summary)
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... (Update)
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Journal of Artificial Intelligence Research 15 (2001).. - Jonathan Baxter Jbaxter
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1.1: Reinforcement Learning for Dynamic Channel Allocation in.. - Satinder Singh (1997)
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0.0: Resource Allocation in Cellular Communication Systems - Tab Le Of
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0.0: Worst-Case Performance of Cellular Channel Assignment Policies - Jordan, Schwabe (1996)
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0.2: Cellular Channel Assignment: Comparing and Simplifying.. - Battiti, Bertossi, Brunato (1997)
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BibTeX entry: (Update)
S. Singh and D. Bertsekas. Reinforcement learning for dynamic channel allocation in cellular telephone systems. In M. C. Mozer, M. I. Jordan, and T. Petsche, editors, NIPS-9, page 974. The MIT Press, 1997. http://citeseer.ist.psu.edu/singh97reinforcement.html More
@inproceedings{ singh97reinforcement,
author = "Satinder Singh and Dimitri Bertsekas",
title = "Reinforcement Learning for Dynamic Channel Allocation in Cellular Telephone Systems",
booktitle = "Advances in Neural Information Processing Systems",
volume = "9",
publisher = "The {MIT} Press",
editor = "Michael C. Mozer and Michael I. Jordan and Thomas Petsche",
pages = "974",
year = "1997",
url = "citeseer.ist.psu.edu/singh97reinforcement.html" }
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