Introducing MiniWorld
MiniWorld is a minimalistic yet versatile 3D simulation environment designed to support research in reinforcement learning and robotics. Developed and maintained by the Farama Foundation, MiniWorld serves as a valuable tool for simulating simple interior environments such as rooms, hallways, and various objects typically found in office and home settings. The project offers a straightforward alternative to more complex simulation environments like VizDoom or DeepMind Lab. One of its defining features is its implementation entirely in Python, making it accessible and easy to modify or extend, especially for students.
Key Features of MiniWorld
MiniWorld comes packed with several features that enhance its utility and accessibility:
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Simplicity and Modifiability: With minimal dependencies, MiniWorld is less prone to breaking and is easy to install. Users can effortlessly create or modify levels, making it an ideal learning platform.
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Performance and Efficiency: It offers excellent performance with high frame rates and supports multiple processes. Its lightweight nature means it requires minimal memory and affords a small download size.
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Open Source Licensing: The tool is provided under the permissive MIT license, ensuring users can freely modify and redistribute it.
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Rich Resources: MiniWorld includes a variety of free 3D models and textures, enhancing the realism of simulations.
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Comprehensive Views: It supports a fully observable top-down or overhead view of environments, which is crucial for certain research applications.
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Domain Randomization: This feature aids in sim-to-real transfer, making simulations more generalizable to real-world scenarios.
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Depth Maps: The simulator can produce depth maps that match camera images (RGB-D), adding depth information to simulations.
Despite its advantages, MiniWorld’s graphics and physics are basic, which limits its use for highly detailed simulations such as those involving robotic arms.
Installation and Usage
To get started with MiniWorld, users need Python 3.7 or higher and dependencies like Gymnasium, NumPy, and Pyglet for 3D graphics. GPU support, while optional, can enhance performance. Installation is straightforward, either through PyPI with a simple command:
python3 -m pip install miniworld
Or by cloning the source from GitHub:
git clone https://github.com/Farama-Foundation/Miniworld.git
cd Miniworld
python3 -m pip install -e .
If any issues arise, a comprehensive troubleshooting guide is available to guide users through common problems.
Simulation Control and Testing
MiniWorld provides a user-friendly UI application for manual control of the simulation or a real-world robot. By using the manual_control.py script, users can easily navigate different environments and view real-time camera feeds. Additional scripts are also available to conduct automated tests and gather performance metrics, ensuring efficient and effective simulation evaluations.
Special Features for Cluster and Online Environments
When running simulations in environments like clusters or Google Colab, MiniWorld supports offscreen rendering. Adjustments, such as setting PYOPENGL_PLATFORM to egl or using xvfb, ensure smooth operation in these contexts.
Publications and Research
MiniWorld has been featured in numerous research publications, underscoring its utility in advancing reinforcement learning and robotics. Studies have explored various aspects such as exploration, offline reinforcement learning, goal-conditioned transfer, and more, highlighting MiniWorld’s versatility in facilitating cutting-edge research.
In summary, MiniWorld represents a balanced fusion of simplicity, flexibility, and performance, making it a valuable resource for researchers and students delving into reinforcement learning and robotics.