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This section provides a hands-on introduction to reinforcement learning (RL) and OpenEnv through a series of interactive tutorials. Whether you're new to RL or looking to learn how OpenEnv simplifies building and deploying environments, these tutorials will guide you through the fundamentals.
**What is OpenEnv?**
OpenEnv is a collaborative effort between **Meta, Hugging Face, Unsloth, GPU Mode, Reflection**, and other industry leaders to standardize reinforcement learning environments. Our goal is to make environment creation as easy and standardized as model sharing on Hugging Face.
Learning Path
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The tutorials are designed to be followed in sequence, building upon concepts from previous lessons:
1. **Introduction & Quick Start** - Understand what OpenEnv is, why it exists, and run your first environment. Includes a comparison with traditional solutions like OpenAI Gym.
2. **Using Environments** - Learn how to connect to environments (Hub, Docker, URL), create AI policies, and run evaluations. Work with different games and multi-player scenarios.
3. **Building & Sharing Environments** - Create your own custom environment from scratch, package it with Docker, and share it on Hugging Face Hub.
4. **Packaging & Deploying** - The complete reference guide for creating, packaging, and deploying custom environments with the ``openenv`` CLI.
5. **Contributing to Hugging Face** - Publish, fork, and contribute to environments hosted as Hugging Face Spaces.
**No GPU Required!** All five tutorials run without a GPU.
For GPU-intensive training workflows, see the :doc:`RL Training Tutorial </tutorials/rl-training-2048>` in the Tutorials section.
Prerequisites
-------------
Before starting, ensure you have:
- Basic Python programming knowledge
- Python 3.11+ installed
- Docker (optional, for container-based deployment)
Running the Tutorials
---------------------
You can run these tutorials locally:
.. code-block:: bash
# Install OpenEnv
pip install openenv-core
# Run the Python scripts
python plot_01_introduction_quickstart.py
Or view them directly in the documentation with full code output below.
.. toctree::
:maxdepth: 1
:caption: Quick Start
plot_01_introduction_quickstart
plot_02_using_environments
plot_03_building_environments
environment-builder
contributing-envs
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