BibTeX Export for Finding Feasible Routes with Reinforcement Learning Using Macro-Level Traffic Measurements (Short Paper)

Copy to Clipboard Download

@InProceedings{ozkan_et_al:LIPIcs.GIScience.2023.58,
  author =	{Ozkan, Mustafa Can and Cheng, Tao},
  title =	{{Finding Feasible Routes with Reinforcement Learning Using Macro-Level Traffic Measurements}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{58:1--58:6},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-288-4},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{277},
  editor =	{Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.58},
  URN =		{urn:nbn:de:0030-drops-189536},
  doi =		{10.4230/LIPIcs.GIScience.2023.58},
  annote =	{Keywords: routing, reinforcement learning, q-learning, data mining, macro-level patterns}
}

The metadata provided by Dagstuhl Publishing on its webpages, as well as their export formats (such as XML or BibTeX) available at our website, is released under the CC0 1.0 Public Domain Dedication license. That is, you are free to copy, distribute, use, modify, transform, build upon, and produce derived works from our data, even for commercial purposes, all without asking permission. Of course, we are always happy if you provide a link to us as the source of the data.

Read the full CC0 1.0 legal code for the exact terms that apply: https://creativecommons.org/publicdomain/zero/1.0/legalcode

Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail