summary/01_introduction_quickstart.md

# 01. Introduction & Quick Start

Source:
- https://meta-pytorch.org/OpenEnv/auto_getting_started/plot_01_introduction_quickstart.html



## Main idea



OpenEnv is a standardized framework for building, sharing, and using RL environments as typed, containerized services.



The official docs frame it as:



- Gym-style interaction

- Docker-based isolation

- typed contracts

- HTTP/WebSocket access

- easy sharing through Hugging Face



## Core loop



The RL interaction model is still the normal loop:



1. reset environment

2. observe state

3. choose action

4. call step

5. receive reward + next observation

6. repeat until done



The difference is that OpenEnv wraps this loop in a typed client/server system.



## Why OpenEnv instead of only Gym



The docs emphasize these advantages:



- type safety

- environment isolation through containers

- better reproducibility

- easier sharing and deployment

- language-agnostic communication

- cleaner debugging



The key contrast is:



- old style: raw arrays and same-process execution

- OpenEnv style: typed objects and isolated environment runtime



## Important mental model



OpenEnv treats environments more like services than in-process libraries.



That means:



- your environment logic can run separately from the agent code

- failures in the environment do not automatically crash the training loop

- deployment and usage are closer to how production systems work



## What this means for `python_env`

Your repo should keep these properties intact:

- typed `Action`, `Observation`, and evaluation models
- a clean environment class with `reset()`, `step()`, and `state`
- a client that hides transport details
- a deployable container

For hackathon purposes, this page is the justification for why your project is not just a script. It is a reusable environment artifact.