  BayesSim is a statistical technique for domain randomization in reinforcement
learning based on likelihood-free inference of simulation parameters. This
paper outlines BayesSimIG: a library that provides an implementation of
BayesSim integrated with the recently released NVIDIA IsaacGym. This
combination allows large-scale parameter inference with end-to-end GPU
acceleration. Both inference and simulation get GPU speedup, with support for
running more than 10K parallel simulation environments for complex robotics
tasks that can have more than 100 simulation parameters to estimate. BayesSimIG
provides an integration with TensorBoard to easily visualize slices of
high-dimensional posteriors. The library is built in a modular way to support
research experiments with novel ways to collect and process the trajectories
from the parallel IsaacGym environments.
