BayesSimIG：使用IsaacGym进行自适应域随机化的可扩展参数推断
BayesSimIG: Scalable Parameter Inference for Adaptive Domain Randomization with IsaacGym
摘要：
BayesSim是一种基于模拟参数的无似然推断的强化学习中域随机化的统计技术。本文概述了BayesSimIG：一个库，它提供了与最近发布的NVIDIA IsaacGym集成的BayesSim实现。这种组合允许通过端到端GPU加速进行大规模参数推断。推理和仿真都获得了GPU加速，支持运行超过10K的并行仿真环境，用于复杂的机器人任务，这些任务可能需要估计100多个仿真参数。BayesSimIG提供了与TensorBoard的集成，可以轻松地可视化高维后验的切片。该库以内置的模块化方式构建，以新颖的方式收集和处理来自并行IsaacGym环境的轨迹，从而支持研究实验。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
