# Gymnasium — Standard API for Reinforcement Learning Environments

> Gymnasium is the maintained fork of OpenAI Gym, providing a universal API for single-agent reinforcement learning environments used in research and education worldwide.

## Install

Save as a script file and run:

# Gymnasium — Standard API for Reinforcement Learning Environments

## Quick Use
```bash
pip install gymnasium
python -c "import gymnasium as gym; env = gym.make('CartPole-v1'); obs, info = env.reset(); print(obs)"
```

## Introduction
Gymnasium is the maintained successor to OpenAI Gym, managed by the Farama Foundation. It provides a standard Python API that every reinforcement learning framework depends on for defining, running, and benchmarking single-agent environments.

## What Gymnasium Does
- Defines a universal `Env` interface (reset, step, render, close) adopted by the entire RL ecosystem
- Ships dozens of built-in environments: classic control, toy text, Box2D, MuJoCo, and Atari via plugins
- Provides wrappers for observation/reward/action transformations without editing environment code
- Supports vectorized environments for parallel rollout collection
- Offers a registration system so third-party environments plug in with a single `gym.make()` call

## Architecture Overview
Every environment subclasses `gymnasium.Env` and implements `reset()` and `step(action)`. Spaces (`Box`, `Discrete`, `Dict`, etc.) describe observation and action shapes. Wrappers stack transparently around any env, and `gymnasium.vector` provides `SyncVectorEnv` and `AsyncVectorEnv` for batched simulation. The registry maps string IDs to entry points, allowing lazy imports of heavy dependencies.

## Self-Hosting & Configuration
- Install from PyPI: `pip install gymnasium` or with extras like `gymnasium[atari]`, `gymnasium[mujoco]`
- No server or daemon needed; it is a pure Python library
- Set `render_mode="human"` for on-screen visualization or `"rgb_array"` for headless rendering
- Configure wrappers via `gymnasium.wrappers` (TimeLimit, RecordVideo, FlattenObservation, etc.)
- Register custom environments with `gymnasium.register(id="MyEnv-v0", entry_point="my_module:MyEnv")`

## Key Features
- Drop-in replacement for OpenAI Gym with a cleaner, fully typed API
- Active maintenance with regular releases and Python 3.8+ support
- Wrappers ecosystem for reward shaping, frame stacking, and auto-reset
- Compatibility shims (`gymnasium.utils.env_checker`) to validate custom envs
- First-class support in Stable Baselines3, CleanRL, RLlib, and most RL libraries

## Comparison with Similar Tools
- **OpenAI Gym** — original project, now deprecated; Gymnasium is the official continuation
- **PettingZoo** — multi-agent environments with a similar API, also by Farama Foundation
- **DeepMind Control Suite** — MuJoCo-based continuous control, narrower scope
- **Brax** — JAX-accelerated physics environments for fast parallel simulation
- **Unity ML-Agents** — 3D game-engine environments, heavier setup

## FAQ
**Q: Is Gymnasium backward-compatible with OpenAI Gym code?**
A: Mostly yes. The API added explicit truncation handling and typing improvements, but a compatibility wrapper exists.

**Q: How do I use Atari environments?**
A: Install `gymnasium[atari]` and `gymnasium[accept-rom-license]`, then `gym.make("ALE/Pong-v5")`.

**Q: Can I use Gymnasium with JAX or PyTorch directly?**
A: Gymnasium itself is NumPy-based, but `gymnasium.experimental.wrappers.JaxToNumpyV0` and community bridges exist.

**Q: Where did the name come from?**
A: It keeps the Gym metaphor while indicating it is a separate, actively maintained project under the Farama Foundation.

## Sources
- https://github.com/Farama-Foundation/Gymnasium
- https://gymnasium.farama.org/

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Author: Script Depot