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BayesSim is a statistical technique for domain randomization in reinforcementlearning based on likelihood-free inference of simulation parameters. Thispaper outlines BayesSimIG: a library that provides an implementation ofBayesSim integrated with the recently released NVIDIA IsaacGym. Thiscombination allows large-scale parameter inference with end-to-end GPUacceleration. Both inference and simulation get GPU speedup, with support forrunning more than 10K parallel simulation environments for complex roboticstasks that can have more than 100 simulation parameters to estimate. BayesSimIGprovides an integration with TensorBoard to easily visualize slices ofhigh-dimensional posteriors. The library is built in a modular way to supportresearch experiments with novel ways to collect and process the trajectoriesfrom the parallel IsaacGym environments.

BayesSimIG: Scalable Parameter Inference for Adaptive Domain Randomization with IsaacGym