In plain words: Driving agents learn purely by competing against themselves in a super-fast simulator that generates 1.6 billion km of practice. Trained with no human driving data, it beats the previous best on recorded real-world scenarios and averages 17.5 years of simulated driving between incidents.
Abstract · Robust Autonomy Emerges from Self-Play
Self-play has powered breakthroughs in two-player and multi-player games. Here we show that self-play is a surprisingly effective strategy in another domain. We show that robust and naturalistic driving emerges entirely from self-play in simulation at unprecedented scale -- 1.6~billion~km of driving. This is enabled by Gigaflow, a batched simulator that can synthesize and train on 42 years of subjective driving experience per hour on a single 8-GPU node. The resulting policy achieves state-of-the-art performance on three independent autonomous driving benchmarks. The policy outperforms the prior state of the art when tested on recorded real-world scenarios, amidst human drivers, without ever seeing human data during training. The policy is realistic when assessed against human references and achieves unprecedented robustness, averaging 17.5 years of continuous driving between incidents in simulation.
Marco Cusumano-Towner, David Hafner, Alex Hertzberg, Brody Huval, Aleksei Petrenko, Eugene Vinitsky, Erik Wijmans, Taylor Killian, Stuart Bowers, Ozan Sener, Philipp Krähenbühl, Vladlen Koltun
arXiv:2502.03349 · cs.LG, cs.AI, cs.RO · submitted Feb 5, 2025
abstract · pdf · html
- All simulated agents use the same neural net with the same weights, albeit with randomized rewards and conditioning vector to allow them to behave as different types of vehicles with different types of aggressiveness. This is like driving in a world where everyone is different copies of you, but some of your copies are in rush while others are patient. This allows backprop to optimize for a sort of global utility across the entire population.
- There is no modeling of occlusion effects. Instead, agents are given the state of nearby agents, but corrupted by random noise. In the real world, occluded nearby agents can be extremely close (think about a child running out from behind a parked car). The paper comments on this.
> Both Waymax and nuPlan construct observations, maps, and other actors with auto-labeling tools from realworld perception data. This brings occlusion, incorrect or missing traffic-light states, and obstacles revealed at the last moment. Despite the minimalistic noise modeling in GIGAFLOW, the GIGAFLOW policy generalizes zero-shot to these conditions.
- The resulting policy simulates agents that are human-like, even though the system has never seen humans drive. This is a great result when one considers other reinforcement learning projects produce extremely high performance agents that humans would consider to be abusive or pathological.