This tutorial is a first in a series of introduction notebooks for WarpDrive, a PyCUDA-based framework for extremely parallelized multi-agent reinforcement learning (RL) on a single graphics processing unit (GPU).

In this tutorial, we describe

• how we harness the GPU's ability to parallelize operations across a large number of RL agents and multiple environment replicas. In conjunction with training logic using Pytorch, we can perform extremely fast end-to-end training of multiple RL agents, all on a single GPU, in just a few lines of code.
• how WarpDrive managers work.