@elliotarledge The network encoder is the bottleneck. This project was done with PufferDrive 2.0. It runs at ~300-400k SPS on a 4090 or 5090. IIRC, using one of the standard PufferLib MLPs gets you 1-1.5 M SPS, but the entity encoder is more performant. Starting from PufferLib 5.0 + spending a little time optimizing the regularizer implementation should get you to ~800k or higher SPS, but the network is still a bottleneck (overview below).

Sweden, here we come! 🇸🇪 I’m heading to ECCV, where we’ll host our workshop this Tuesday: emerging-ad.github.io/ We have a fantastic lineup of speakers; see you there!

Refactoring efforts pay off 🙂 (same functionality; less and more readable code)

Great video for an introduction to simulating fluids. youtube.com/watch?v=rSKMYc1C…

Looking forward to presenting our work on spiced self-play online on the 31st of July! Sign-up link below

for those interested, full interview youtube.com/watch?v=n-hWHV2Z…

We provide a couple of comparisons on the webpage! spiced-self-play.com/ e.g., see yellow car here

Project page: spiced-self-play.com/ Arxiv: arxiv.org/abs/2606.19370

New Paper: Human-like Autonomy Emerges from Self-Play and a Pinch of Human Data. We trained self-play RL on 60 years of simulation on 1 GPU in ~15 hours. Regularizing with 30 minutes of demonstration data produces much more human-like driving policies!

Two of my cartoons made it into Nature Communications! Glad to have collaborated on this work, and congratulations to the authors on the publication.

New preprint led by @saeedrmd! “Artificial Intelligence for Modeling and Simulation of Mixed Automated and Human Traffic." Drawing on 200+ papers, it offers a snapshot of where the field stands and outlines promising directions ahead.

I'm feeling this one today

Benchmarks are a key driver of progress. But how should we evaluate human-like driving? Does the Waymo Open Sim Agent Challenge (WOSAC) really capture what matters? Looking forward to any feedback!