Less Can Be More: Pruning Street Networks for Sustainable City Making

Sánchez-Vaquerizo, Javier Argota; Helbing, Dirk

doi:10.2139/ssrn.4291171

Cited by 1 publication

(1 citation statement)

References 105 publications

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“…This feature is also stated in the reverse: that the removal of a road reduces congestion in the network. Braess's Paradox characterizes a many player social dilemma where independent and self-interested agents suffer without coordination [59]. All agents are trying to be faster, but in doing so they end up being slower.…”

Section: Learning In Braess's Paradox a Braess's Paradoxmentioning

confidence: 99%

Counter-Intuitive Effects of Q-Learning Exploration in a Congestion Dilemma

Carissimo

2024

IEEE Access

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Exploration is an integral part of learning dynamics which allows algorithms to search a space of solutions. When many algorithms simultaneously explore, this can lead to counter-intuitive effects. This paper contributes an analysis of the influence that exploration has on a multi-agent system of Q-learners in a famous congestion dilemma, the Braess paradox. I find ranges of the exploration rate for which ϵgreedy Q-learners show chaotic and oscillatory dynamics which do not converge, and yield better than Nash equilibrium results. I decouple the dynamics endogenous to Q-learning from the exogenous exploration rate ϵ, and find that Q-learners implicitly coordinate with low exploration rates ϵ ∈ (0, 0.1), but are disrupted in their coordination for larger exploration rates ϵ > 0.1. The best implicit coordination leads to a 20% reduction in average travel times which approaches the social optimum. I discuss how our results may inform multi-agent algorithm design, fit within a cognitive science perspective of cognitive noise during learning, and provide a mechanistic hypothesis for the lack of empirical evidence of the Braess Paradox in traffic systems.

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Section: Learning In Braess's Paradox a Braess's Paradoxmentioning

confidence: 99%

Counter-Intuitive Effects of Q-Learning Exploration in a Congestion Dilemma

Carissimo

2024

IEEE Access

View full text Add to dashboard Cite

show abstract

Less Can Be More: Pruning Street Networks for Sustainable City Making

Cited by 1 publication

References 105 publications

Counter-Intuitive Effects of Q-Learning Exploration in a Congestion Dilemma

Counter-Intuitive Effects of Q-Learning Exploration in a Congestion Dilemma

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