Principle:Isaac sim IsaacGymEnvs Environment Setup
| Sources | Domains | Last Updated |
|---|---|---|
| IsaacGymEnvs | Setup, DevOps | 2026-02-15 00:00 GMT |
Overview
How to install IsaacGymEnvs and its dependencies to enable GPU-accelerated reinforcement learning training with NVIDIA Isaac Gym.
Description
Setting up the IsaacGymEnvs development environment requires an editable pip install of the repository, with a prerequisite installation of the Isaac Gym Preview release from NVIDIA. The environment targets systems equipped with an NVIDIA GPU capable of running PhysX and CUDA-based tensor operations. The editable install mode allows developers to modify task code, configuration files, and training scripts without reinstalling the package after each change.
Key dependencies include PyTorch (with CUDA support), rl-games (the RL training framework), Hydra/OmegaConf (for hierarchical configuration), and several geometry and physics utility libraries (warp-lang, urdfpy, pysdf, trimesh). The setup.py at the repository root declares all required packages and their version constraints.
Usage
Use this principle when starting with IsaacGymEnvs for the first time, setting up a new development machine, or creating a reproducible training environment (e.g., in a Docker container or on a cloud GPU instance).
Theoretical Basis
The setup follows standard Python packaging with setuptools. The editable install (pip install -e .) creates a link from the Python site-packages directory back to the source tree, so that any source code modifications take effect immediately without a reinstall. This is the recommended development workflow for Python packages that are under active modification, as opposed to a regular install which copies files into site-packages.
The dependency chain is layered: Isaac Gym Preview provides the low-level GPU physics simulation C++/Python bindings, PyTorch provides the tensor computation backend, rl-games provides the RL algorithm implementations (PPO, SAC), and Hydra provides the configuration composition framework. IsaacGymEnvs sits on top of all of these, providing the task definitions, reward functions, and training orchestration.