How Bad is Selfish Driving? Bounding the Inefficiency of Equilibria in
Urban Driving Games
- URL: http://arxiv.org/abs/2210.13064v1
- Date: Mon, 24 Oct 2022 09:32:40 GMT
- Title: How Bad is Selfish Driving? Bounding the Inefficiency of Equilibria in
Urban Driving Games
- Authors: Alessandro Zanardi, Pier Giuseppe Sessa, Nando K\"aslin, Saverio
Bolognani, Andrea Censi, Emilio Frazzoli
- Abstract summary: We study the (in)efficiency of any equilibrium players might agree to play.
We obtain guarantees that refine existing bounds on the Price of Anarchy.
Although the obtained guarantees concern open-loop trajectories, we observe efficient equilibria even when agents employ closed-loop policies.
- Score: 64.71476526716668
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We consider the interaction among agents engaging in a driving task and we
model it as general-sum game. This class of games exhibits a plurality of
different equilibria posing the issue of equilibrium selection. While selecting
the most efficient equilibrium (in term of social cost) is often impractical
from a computational standpoint, in this work we study the (in)efficiency of
any equilibrium players might agree to play. More specifically, we bound the
equilibrium inefficiency by modeling driving games as particular type of
congestion games over spatio-temporal resources. We obtain novel guarantees
that refine existing bounds on the Price of Anarchy (PoA) as a function of
problem-dependent game parameters. For instance, the relative trade-off between
proximity costs and personal objectives such as comfort and progress. Although
the obtained guarantees concern open-loop trajectories, we observe efficient
equilibria even when agents employ closed-loop policies trained via
decentralized multi-agent reinforcement learning.
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