V-Max: A Reinforcement Learning Framework for Autonomous Driving

• URL: http://arxiv.org/abs/2503.08388v3
• Date: Thu, 17 Jul 2025 15:30:27 GMT
• Title: V-Max: A Reinforcement Learning Framework for Autonomous Driving
• Authors: Valentin Charraut, Waël Doulazmi, Thomas Tournaire, Thibault Buhet,
• Abstract summary: V-Max is an open research framework providing all the necessary tools to makeReinforcement Learning practical for Autonomous Driving.<n>V-Max is built on Waymax, a hardware-accelerated AD simulator designed for large-scale experimentation.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Learning-based decision-making has the potential to enable generalizable Autonomous Driving (AD) policies, reducing the engineering overhead of rule-based approaches. Imitation Learning (IL) remains the dominant paradigm, benefiting from large-scale human demonstration datasets, but it suffers from inherent limitations such as distribution shift and imitation gaps. Reinforcement Learning (RL) presents a promising alternative, yet its adoption in AD remains limited due to the lack of standardized and efficient research frameworks. To this end, we introduce V-Max, an open research framework providing all the necessary tools to make RL practical for AD. V-Max is built on Waymax, a hardware-accelerated AD simulator designed for large-scale experimentation. We extend it using ScenarioNet's approach, enabling the fast simulation of diverse AD datasets.

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