Simulation

Parallel Simulation

Parallel simulation is the practice of running thousands of physics environments simultaneously, typically on a single GPU, so that reinforcement learning agents can gather experience orders of magnitude faster than real time. NVIDIA's Isaac Gym demonstrated an end-to-end GPU pipeline in which simulation, observation processing, and policy updates all stay on-device, reducing training times for locomotion and manipulation policies from weeks to minutes or hours.

Why it matters for physical AI

Massively parallel rollouts made sim-trained reinforcement learning practical for real robots: policies for quadrupeds and humanoids are now routinely trained across thousands of randomized environments before sim-to-real transfer.