V-Max为自动驾驶强化学习提供高效开源框架，解决训练难问题。
V-Max: A Reinforcement Learning Framework for Autonomous Driving
- 基于硬件加速仿真器Waymax构建，支持大规模实验
- 集成ScenarioNet实现多样驾驶场景快速生成
- 推动强化学习在自动驾驶中的实用化，适合研究者使用
基于学习的决策方法有望实现可泛化的自动驾驶策略，降低规则驱动方法的工程负担。模仿学习（IL）仍是主流范式，得益于大规模人类示范数据集，但存在分布偏移和模仿差距等固有局限。强化学习（RL）是潜在替代方案，但其在自动驾驶领域的应用受限于缺乏标准化且高效的科研框架。为此，我们提出V-Max，一个开放的研究框架，提供使强化学习在自动驾驶中实用化的全套工具。V-Max基于Waymax构建，这是一个专为大规模实验设计的硬件加速自动驾驶仿真器。我们采用ScenarioNet的方法扩展它，实现多样化自动驾驶数据集的快速仿真。
原文摘要 · Abstract (English)
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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