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Reinforcement Learning for Dynamic Channel Allocation in Cellular Telephone Systems (1997)  (Make Corrections)  (55 citations)
Satinder Singh, Dimitri Bertsekas
Advances in Neural Information Processing Systems



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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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Least-Squares Temporal Difference Learning - Justin Boyan Nasa   (Correct)
Journal of Artificial Intelligence Research 15 (2001).. - Jonathan Baxter Jbaxter   (Correct)
Journal of Artificial Intelligence Research 15 (2001).. - Jonathan Baxter Jbaxter   (Correct)

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1.1:   Reinforcement Learning for Dynamic Channel Allocation in.. - Satinder Singh (1997)   (Correct)
0.0:   Resource Allocation in Cellular Communication Systems - Tab Le Of   (Correct)
0.0:   Worst-Case Performance of Cellular Channel Assignment Policies - Jordan, Schwabe (1996)   (Correct)

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0.2:   Cellular Channel Assignment: Comparing and Simplifying.. - Battiti, Bertossi, Brunato (1997)   (Correct)
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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" }
Citations (may not include all citations):
563   Learning to predict by the methods of temporal differences - Sutton - 1988
257   Learning to act using real-time dynamic programming - Barto, Bradtke et al. - 1995
219   Practical issues in temporal difference learning - Tesauro - 1992
124   Improving elevator performance using reinforcement learning - Crites, Barto - 1996
54   Comparisons of Channel-Assignment Strategies in Cellular Mob.. (context) - Zhang, Yum - 1989
30   High-performance job-shop scheduling with a time-delay TD - Zhang, Dietterich - 1996
13   Performance limits for channelized cellular telephone system.. (context) - McEliece, Sivarajan - 1994
2   Dynamic Programming and Optimal Control: Vols 1 and 2 (context) - Bertsekas - 1995
2   Neuro-Dynamic Programming Athena-Scientific (context) - Bertsekas, Tsitsiklis - 1996
1   A dynamic channel allocation technique based on Hopfield Neu.. (context) - is, Processing et al. - 1996



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Markov Games as a Framework for Multi-Agent Reinforcement Learning - Littman (1994)   (Correct)
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