ML Anthology

Enhancing Smart, Sustainable Mobility with Game Theory and Multi-Agent Reinforcement Learning with Applications to Ridesharing

Lucia Cipolina-Kun
AAAI 2023 pp. 16113-16114
doi:10.1609/AAAI.V37I13.26917 /aaai/2023/cipolinakun2023aaai-enhancing/

Abstract

We propose the use of game-theoretic solutions and multi- agent Reinforcement Learning in the mechanism design of smart, sustainable mobility services. In particular, we present applications to ridesharing as an example of a cost game.

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Cite

Text

Cipolina-Kun. "Enhancing Smart, Sustainable Mobility with Game Theory and Multi-Agent Reinforcement Learning with Applications to Ridesharing." AAAI Conference on Artificial Intelligence, 2023. doi:10.1609/AAAI.V37I13.26917

Markdown

[Cipolina-Kun. "Enhancing Smart, Sustainable Mobility with Game Theory and Multi-Agent Reinforcement Learning with Applications to Ridesharing." AAAI Conference on Artificial Intelligence, 2023.](https://mlanthology.org/aaai/2023/cipolinakun2023aaai-enhancing/) doi:10.1609/AAAI.V37I13.26917

BibTeX

@inproceedings{cipolinakun2023aaai-enhancing,
  title     = {{Enhancing Smart, Sustainable Mobility with Game Theory and Multi-Agent Reinforcement Learning with Applications to Ridesharing}},
  author    = {Cipolina-Kun, Lucia},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2023},
  pages     = {16113-16114},
  doi       = {10.1609/AAAI.V37I13.26917},
  url       = {https://mlanthology.org/aaai/2023/cipolinakun2023aaai-enhancing/}
}