Abstract

Traffic congestions present a major challenge in large cities. Consid- ering the distributed, self-interested nature oftraffic we tackle congestions using multiagent reinforcement learning (MARL). In this thesis, we advance the state- of-the-art by delivering the first MARL convergence guarantees in congestion- like problems. We introduce an algorithm through which drivers can learn opti- mal routes by locally estimating the regret associated with their decisions, which we prove to converge to an equilibrium. In order to mitigate the effects ofselfish- ness, we also devise a decentralised tolling scheme, which we prove to minimise traffic congestion levels. Our theoretical results are supported by an extensive empirical evaluation on realistic traffic networks. 1.


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The different versions of the original document can be found in:

http://dx.doi.org/10.5753/ctd.2019.6332
https://academic.microsoft.com/#/detail/2888285943
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Published on 01/01/2019

Volume 2019, 2019
DOI: 10.5753/ctd.2019.6332
Licence: CC BY-NC-SA license

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