Isaac Gym: End-to-End GPU-Accelerated Reinforcement Learning

We'll explore NVIDIA's Isaac Gym environment for high-performance reinforcement learning on GPU. We will review key API features, demonstrate examples of training agents, and provide updates on future integration of Isaac Gym functionality within the NVIDIA Omniverse platform. We will demonstrate how to create environments with thousands of agents to train in parallel, and how the Isaac Gym system allows developers to create tensor based views of physics state for all environments. We will also demonstrate the application of physics based domain randomization in Isaac Gym, which can help with sim2real transfer of learned policies to physical robots.