Pictura让自动驾驶策略直接从相机图像自对弈学，跑完3500万公里，性能逼近用真实位置速度的版本，值得AI研究员看看。
Pictura是一个GPU加速的多智能体驾驶模拟器，在单张H100上每秒可处理50万智能体步（200万张图像）。它直接渲染每个智能体的第一人称视角，消除了传统特权观测带来的表征鸿沟。基于Pictura，研究者用纯PPO训练了Alberti策略，历经500亿智能体步（约3500万公里驾驶）。该策略首次实现直接从透视图像进行大规模自对弈学习，性能接近特权向量化基线，并在Waymo Open Motion Dataset渲染场景中零样本超越特权智能体。
Pictura: Perspective-View Self-Play at Scale for Driving
Self-play in simulation produces robust driving policies at scale. Demonstrations of such behavior have been made using privileged vectorized observations such as exact poses and velocities, even for occluded agents. This assumes that perception is solved and introduces a representation gap with the partial observation of a deployed agent driving from the perspective view of egocentric cameras. A common fix, distilling the privileged policy into a camera-input student, leaves the student imitating decisions its own view cannot justify. Instead, we establish perspective-view self-play as a practical training regime. We introduce Pictura, a GPU-accelerated multi-agent driving simulator that renders each agent's egocentric view at every step, mitigating the representation gap at its source. Pictura sustains up to 500K agent-steps/s (2M images/s) on a single H100. Using Pictura, we train Alberti by self-play with plain PPO. It is the first large-scale driving self-play policy trained directly from perspective images, without privileged observations. Training spans 50B agent steps for ~35M km of driving. It approaches the driving performance of its privileged vectorized counterpart, and transfers zero-shot to Waymo Open Motion Dataset layouts re-rendered in Pictura, where it outperforms privileged vectorized agents. Project page: https://valeoai.github.io/Pictura/