Scaling Self-Play for End-to-End Driving
End-to-end autonomous driving models are typically trained on offline human-demonstration datasets that provide limited state coverage and often no closed-loop feedback, making them prone to compounding errors when deployed in closed-loop and brittle to long-tail agent interactions. To overcome these limitations, we propose an alternative strategy for training end-to-end driving models: large-scale self-play directly from pixels in simulation. While prior self-play approaches have shown promising transfer to real-world driving, they typically assume vectorized Bird's-Eye-View (BEV) observations that are incompatible with end-to-end policies operating directly on sensor observations. To th...
| Authors | Luke Rowe, Roger Girgis, Rodrigue de Schaetzen et al. |
|---|---|
| Primary category | cs.RO |
| Published | 17 June 2026 |
| Last updated at source | 19 June 2026 |
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