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

Authors

  • Lucia Cipolina-Kun University of Bristol

DOI:

https://doi.org/10.1609/aaai.v37i13.26917

Keywords:

Multi Agent Reinforcement Learning, PhD Thesis, Abstract

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.
AAAI-23 Proceedings Cover

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Published

2023-09-06

How to Cite

Cipolina-Kun, L. (2023). Enhancing Smart, Sustainable Mobility with Game Theory and Multi-Agent Reinforcement Learning With Applications to Ridesharing. Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 16113-16114. https://doi.org/10.1609/aaai.v37i13.26917

Issue

Vol. 37 No. 13: AAAI-23 Special Programs, IAAI-23, EAAI-23, Student Papers and Demonstrations

Section

AAAI Doctoral Consortium Track