Excited to share our work about the MVT, a large-scale highway field test for which we trained autonomous vehicles to smooth out traffic flow using deep reinforcement learning, then deployed our AI controllers onto 100 vehicles in busy morning traffic. Read more below! (1/n)

We’re open-sourcing and arxiving GPUDrive, a GPU-accelerated 2.5D multi-agent driving simulator that runs at over a million FPS. Hundreds of scenes on one GPU means scalable multi-agent planning

Another driving sim??

Can language models be trained to find solutions to as yet unsolved mathematical problems? The answer is yes! Check our new article 🙂 1/n

x.com/i/article/1853…

Tic-Tac-Toe... but the opponent's moves are hidden. Can you outsmart our top RL agents? Play here: nathanlichtle.com/research/2p0s

This paper has everything: large-scale empirical testing, new benchmarks, and subsequent empirical recommendations that we think might make solving imperfect information games a little easier

New Blog Post: Scaling Up Reinforcement Learning for Traffic Smoothing: A 100-AV Highway Deployment bair.berkeley.edu/blog/2025/03/2…