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On suitability of the reinforcement learning methodology in dynamic, heterogeneous, self-optimizing networks

Milos Rovcanin (UGent) , Eli De Poorter (UGent) , Ingrid Moerman (UGent) and Piet Demeester (UGent)
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Citation

Please use this url to cite or link to this publication:

MLA
Rovcanin, Milos et al. “On Suitability of the Reinforcement Learning Methodology in Dynamic, Heterogeneous, Self-optimizing Networks.” Lecture Notes in Computer Science. Vol. 8121. 2013. 162–175. Print.
APA
Rovcanin, M., De Poorter, E., Moerman, I., & Demeester, P. (2013). On suitability of the reinforcement learning methodology in dynamic, heterogeneous, self-optimizing networks. LECTURE NOTES IN COMPUTER SCIENCE (Vol. 8121, pp. 162–175). Presented at the 13th International Conference on Next Generation Wired/Wireless Advanced Networking, Lecture Notes in Computer Science (NEW2AN - 2013).
Chicago author-date
Rovcanin, Milos, Eli De Poorter, Ingrid Moerman, and Piet Demeester. 2013. “On Suitability of the Reinforcement Learning Methodology in Dynamic, Heterogeneous, Self-optimizing Networks.” In Lecture Notes in Computer Science, 8121:162–175.
Chicago author-date (all authors)
Rovcanin, Milos, Eli De Poorter, Ingrid Moerman, and Piet Demeester. 2013. “On Suitability of the Reinforcement Learning Methodology in Dynamic, Heterogeneous, Self-optimizing Networks.” In Lecture Notes in Computer Science, 8121:162–175.
Vancouver
1.
Rovcanin M, De Poorter E, Moerman I, Demeester P. On suitability of the reinforcement learning methodology in dynamic, heterogeneous, self-optimizing networks. LECTURE NOTES IN COMPUTER SCIENCE. 2013. p. 162–75.
IEEE
[1]
M. Rovcanin, E. De Poorter, I. Moerman, and P. Demeester, “On suitability of the reinforcement learning methodology in dynamic, heterogeneous, self-optimizing networks,” in LECTURE NOTES IN COMPUTER SCIENCE, Sint-Petersburg, Russia, 2013, vol. 8121, pp. 162–175.
@inproceedings{4190760,
  author       = {Rovcanin, Milos and De Poorter, Eli and Moerman, Ingrid and Demeester, Piet},
  booktitle    = {LECTURE NOTES IN COMPUTER SCIENCE},
  issn         = {0302-9743},
  keywords     = {IBCN},
  language     = {eng},
  location     = {Sint-Petersburg, Russia},
  pages        = {162--175},
  title        = {On suitability of the reinforcement learning methodology in dynamic, heterogeneous, self-optimizing networks},
  volume       = {8121},
  year         = {2013},
}