SHRED is a framework that trains vehicles, pedestrians, and cyclists through large‑scale heterogeneous self‑play from random initialization, without using expert demonstrations or logged trajectories. By combining type‑specific dynamics, procedural scenario generation, and per‑agent randomization, SHRED allows diverse, realistic behaviors to emerge through closed‑loop interaction. In benchmarks such as the Waymo Open Sim Agents Challenge and InterPlan, SHRED outperforms demonstration‑free baselines and prior self‑play methods, showing that heterogeneity in self‑play improves both traffic realism and planning performance.
By Wei-Jer Chang, Zhouchonghao Wu, Akshay Rangesh, Weixin Li, Masayoshi Tomizuka, Wei Zhan
arXiv:2606. 19370v1 Announce Type: cross Abstract: Self-play reinforcement learning has recently emerged as a way to train driving policies without any human data.
By Daphne Cornelisse, Julian Hunt, Zixu Zhang, Wa\"el Doulazmi, Kevin Joseph, Jaime Fern\'andez Fisac, Eugene Vinitsky
arXiv:2510. 12560v2 Announce Type: replace-cross Abstract: End-to-end autonomous driving models trained with imitation learning (IL) often generalize poorly, particularly in long-tail scenarios where expert demonstrations are sparse.
By Xiaoji Zheng, Ziyuan Yang, Yanhao Chen, Yuhang Peng, Yuanrong Tang, Gengyuan Liu, Bokui Chen, Jiangtao Gong
arXiv:2607. 13028v1 Announce Type: cross Abstract: Training robust autonomous driving agents requires a simulator that is fast enough for reinforcement learning at scale, realistic enough to ground behavior in real-world map structure, and diverse enough to cover the safety-critical long tail that logged data rarely contains.
By Zhouchonghao Wu, Akshay Rangesh, Weixin Li, Wei-Jer Chang, Zachary Lee, Tim Wang, Wei Zhan
The paper reports on training autonomous driving policies via self‑play, extending previous work by using Transformers and a real‑city high‑definition map. On CARLA and Waymo benchmarks, the resulting policies underperform compared to Gigaflow, with identified failure modes such as reward hacking at traffic lights and lack of incentive to stop at stop signs. The authors also analyze which traffic rules emerge from self‑play and confirm that reward conditioning produces diverse driving behaviors.
By Laur Sisask, Ardi Tampuu, Tambet Matiisen
arXiv:2508. 16947v2 Announce Type: replace-cross Abstract: Despite significant progress, imitation learning-based autonomous driving planners remain largely restricted to reproducing high-frequency biased behaviors, overlooking the inherent behavioral diversity of human driving.
By Fan Ding, Xuewen Luo, Fucai Ke, Hwa Hui Tew, Susilawati Susilawati, Vishnu Monn Baskaran, Junn Yong Loo