V-Max: A Reinforcement Learning Framework for Autonomous Driving
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866911061736685568 |
|---|---|
| author | Charraut, Valentin Doulazmi, Waël Tournaire, Thomas Buhet, Thibault |
| author_facet | Charraut, Valentin Doulazmi, Waël Tournaire, Thomas Buhet, Thibault |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_08388 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | V-Max: A Reinforcement Learning Framework for Autonomous Driving Charraut, Valentin Doulazmi, Waël Tournaire, Thomas Buhet, Thibault Machine Learning Artificial Intelligence Robotics 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. |
| title | V-Max: A Reinforcement Learning Framework for Autonomous Driving |
| topic | Machine Learning Artificial Intelligence Robotics |
| url | https://arxiv.org/abs/2503.08388 |