Behaviorally Diverse Traffic Simulation via Reinforcement Learning

Shinya Shiroshita
Shinya Shiroshita
Shirou Maruyama
Shirou Maruyama
Daisuke Nishiyama
Daisuke Nishiyama
Mario Ynocente Castro
Mario Ynocente Castro
Karim Hamzaoui
Karim Hamzaoui
Jonathan DeCastro
Jonathan DeCastro
Cited by: 0|Bibtex|Views1
Other Links: arxiv.org

Abstract:

Traffic simulators are important tools in autonomous driving development. While continuous progress has been made to provide developers more options for modeling various traffic participants, tuning these models to increase their behavioral diversity while maintaining quality is often very challenging. This paper introduces an easily-tu...More

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