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Merge branch 'main' into OpenEnv-Forge
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‎.env.example‎

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# Copy this file to .env and customize as needed
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# Gitea Service Configuration
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GITEA_URL=http://host.docker.internal:3000
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GITEA_USERNAME=gitea
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GITEA_PASSWORD=gitea123

‎.github/workflows/deploy-hf-env.yml‎

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# Cleanup
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cd ..
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rm -rf hf-space
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rm -rf hf-staging_$ENV_NAME_$ENV_NAME
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rm -rf hf-staging_$ENV_NAME
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# Job to deploy single environment
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deploy-single:

‎.github/workflows/docker-build.yml‎

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dockerfile: src/envs/sumo_rl_env/server/Dockerfile
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- name: atari-env
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dockerfile: src/envs/atari_env/server/Dockerfile
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- name: git-env
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dockerfile: src/envs/git_env/server/Dockerfile
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steps:
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- name: Checkout code

‎.github/workflows/openspiel_base_build.yml‎

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name: Build OpenSpiel Base Image
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on:
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workflow_dispatch: # Manual trigger only
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inputs:
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force_rebuild:
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description: 'Force rebuild even if base image exists'
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required: false
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default: 'false'
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type: boolean
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env:
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REGISTRY: ghcr.io
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IMAGE_PREFIX: ${{ github.repository_owner }}/openenv
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jobs:
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# Job 1: Build base image first
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build-base:
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runs-on: ubuntu-latest
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permissions:
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contents: read
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packages: write
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steps:
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- name: Checkout code
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uses: actions/checkout@v4
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- name: Set up Docker Buildx
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uses: docker/setup-buildx-action@v3
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- name: Log in to GHCR
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uses: docker/login-action@v3
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with:
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registry: ${{ env.REGISTRY }}
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username: ${{ github.actor }}
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password: ${{ secrets.GITHUB_TOKEN }}
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- name: Extract metadata for base image
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id: meta
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uses: docker/metadata-action@v5
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with:
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images: ${{ env.REGISTRY }}/${{ env.IMAGE_PREFIX }}-base
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tags: |
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type=raw,value=latest,enable={{is_default_branch}}
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type=sha
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- name: Build and push base image
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uses: docker/build-push-action@v5
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with:
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context: .
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file: src/core/containers/images/Dockerfile
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push: true
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platforms: linux/amd64,linux/arm64
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tags: ${{ steps.meta.outputs.tags }}
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labels: ${{ steps.meta.outputs.labels }}
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cache-from: type=gha,scope=base
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cache-to: type=gha,mode=max,scope=base
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# Job 2: Build OpenSpiel base image (depends on base)
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build-openspiel-base:
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runs-on: 8-core-ubuntu
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needs: build-base # Wait for base image to be built
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permissions:
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contents: read
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packages: write
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steps:
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- name: Checkout code
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uses: actions/checkout@v4
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- name: Set up Docker Buildx
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uses: docker/setup-buildx-action@v3
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- name: Log in to GHCR
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uses: docker/login-action@v3
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with:
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registry: ${{ env.REGISTRY }}
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username: ${{ github.actor }}
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password: ${{ secrets.GITHUB_TOKEN }}
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- name: Extract metadata for OpenSpiel base image
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id: meta-openspiel-base
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uses: docker/metadata-action@v5
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with:
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images: ${{ env.REGISTRY }}/${{ env.IMAGE_PREFIX }}-openspiel-base
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tags: |
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type=raw,value=latest,enable={{is_default_branch}}
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type=sha
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- name: Build and push OpenSpiel base image
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uses: docker/build-push-action@v5
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with:
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context: .
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file: src/envs/openspiel_env/server/Dockerfile.openspiel-base
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push: true
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platforms: linux/amd64,linux/arm64
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tags: ${{ steps.meta-openspiel-base.outputs.tags }}
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labels: ${{ steps.meta-openspiel-base.outputs.labels }}
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cache-from: type=gha,scope=openspiel-base
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cache-to: type=gha,mode=max,scope=openspiel-base
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- name: Build summary
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run: |
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echo "✅ OpenSpiel base image built and pushed successfully!"
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echo "📦 Image: ${{ steps.meta-openspiel-base.outputs.tags }}"
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echo "🚀 Next regular build will use this new base image"

‎README.md‎

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An e2e framework for creating, deploying and using isolated execution environments for agentic RL training, built using Gymnasium style simple APIs.
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[![PyPI](https://img.shields.io/pypi/v/openenv-core?color=blue)](https://pypi.org/project/openenv-core/)
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[![Discord](https://img.shields.io/badge/Discord-OpenEnv-7289da?style=flat&logo=discord&logoColor=white)](https://discord.gg/YsTYBh6PD9)
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[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/meta-pytorch/OpenEnv/blob/main/examples/OpenEnv_Tutorial.ipynb) **← Try the Interactive Tutorial!**
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---
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**🚀 Featured:** Train LLMs to play BlackJack using [torchforge](https://github.com/meta-pytorch/torchforge) (PyTorch's agentic RL framework): [`examples/grpo_blackjack/`](examples/grpo_blackjack/)
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---
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## OpenEnv on partner platforms:
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- [Lightning AI Studio](https://lightning.ai/environments?section=featured)
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- TRL Example (Coming Soon)!
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- Unsloth Google Colab (Coming soon)!
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## Overview
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OpenEnv provides a standard for interacting with agentic execution environments via simple Gymnasium style APIs - step(), reset(), state(). Users of agentic execution environments can interact with the environment during RL training loops using these simple APIs.
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## Community support & Acknowledgments
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This is an open and community centric project. If you would like to add your name here, please put up a pull request and tag @jspisak for review. Ty!!
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Supporters include: Meta-PyTorch, Hugging Face, [Surge AI](https://surgehq.ai), Unsloth AI, Reflection AI, vLLM, SkyRL (UC-Berkeley), LightningAI, Axolotl AI, ..
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Supporters include: Meta-PyTorch, Hugging Face, [Patronus AI](https://patronus.ai), [Surge AI](https://surgehq.ai), [LastMile AI](https://www.lastmileai.dev), Unsloth AI, Reflection AI, vLLM, SkyRL (UC-Berkeley), LightningAI, Axolotl AI, Stanford Scaling Intelligence Lab, Mithril, ..
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And we'd also like to acknowledge the team at Farama Foundation as the OpenEnv API was heavily inspired by the work you all have done on Gymnasium. Cheers!
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