https://github.com/allenai/allenact
An open source framework for research in Embodied-AI from AI2.
https://github.com/allenai/allenact
ai ai2 computer-vision deep-learning embodied-agent python reinforcement-learning research
Last synced: 4 months ago
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An open source framework for research in Embodied-AI from AI2.
- Host: GitHub
- URL: https://github.com/allenai/allenact
- Owner: allenai
- License: other
- Created: 2020-01-14T21:58:30.000Z (about 6 years ago)
- Default Branch: main
- Last Pushed: 2025-08-22T08:49:48.000Z (6 months ago)
- Last Synced: 2025-10-05T19:47:53.638Z (4 months ago)
- Topics: ai, ai2, computer-vision, deep-learning, embodied-agent, python, reinforcement-learning, research
- Language: Python
- Homepage: https://www.allenact.org
- Size: 25.5 MB
- Stars: 370
- Watchers: 8
- Forks: 58
- Open Issues: 56
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Roadmap: ROADMAP.md
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README
An open source framework for research in Embodied AI
[](./LICENSE)
[](https://allenact.org)
[](https://github.com/allenai/allenact/releases/latest)
[](https://www.python.org/downloads/release/python-360/)
[](https://lgtm.com/projects/g/allenai/allenact/context:python)
[](https://github.com/psf/black)
**AllenAct** is a modular and flexible learning framework designed with a focus on the unique requirements of Embodied-AI research. It provides first-class support for a growing collection of embodied environments, tasks and algorithms, provides reproductions of state-of-the-art models and includes extensive documentation, tutorials, start-up code, and pre-trained models.
AllenAct is built and backed by the [Allen Institute for AI (AI2)](https://allenai.org/). AI2 is a non-profit institute with the mission to contribute to humanity through high-impact AI research and engineering.
## Quick Links
- [Website & Docs](https://www.allenact.org/)
- [Github](https://github.com/allenai/allenact)
- [Install](https://www.allenact.org/installation/installation-allenact/)
- [Tutorials](https://www.allenact.org/tutorials/)
- [AllenAct Paper](https://arxiv.org/abs/2008.12760)
- [Citation](#citation)
## Features & Highlights
* _Support for multiple environments_: Support for the [iTHOR](https://ai2thor.allenai.org/ithor/), [RoboTHOR](https://ai2thor.allenai.org/robothor/) and [Habitat](https://aihabitat.org/) embodied environments as well as for grid-worlds including [MiniGrid](https://github.com/maximecb/gym-minigrid).
* _Task Abstraction_: Tasks and environments are decoupled in AllenAct, enabling researchers to easily implement a large variety of tasks in the same environment.
* _Algorithms_: Support for a variety of on-policy algorithms including [PPO](https://arxiv.org/pdf/1707.06347.pdf), [DD-PPO](https://arxiv.org/pdf/1911.00357.pdf), [A2C](https://arxiv.org/pdf/1611.05763.pdf), Imitation Learning and [DAgger](https://www.ri.cmu.edu/pub_files/2011/4/Ross-AISTATS11-NoRegret.pdf) as well as offline training such as offline IL.
* _Sequential Algorithms_: It is trivial to experiment with different sequences of training routines, which are often the key to successful policies.
* _Simultaneous Losses_: Easily combine various losses while training models (e.g. use an external self-supervised loss while optimizing a PPO loss).
* _Multi-agent support_: Support for multi-agent algorithms and tasks.
* _Visualizations_: Out of the box support to easily visualize first and third person views for agents as well as intermediate model tensors, integrated into Tensorboard.
* _Pre-trained models_: Code and models for a number of standard Embodied AI tasks.
* _Tutorials_: Start-up code and extensive tutorials to help ramp up to Embodied AI.
* _First-class PyTorch support_: One of the few RL frameworks to target PyTorch.
* _Arbitrary action spaces_: Supporting both discrete and continuous actions.
|Environments|Tasks|Algorithms|
|------------|-----|----------|
|[iTHOR](https://ai2thor.allenai.org/ithor/), [RoboTHOR](https://ai2thor.allenai.org/robothor/), [Habitat](https://aihabitat.org/), [MiniGrid](https://github.com/maximecb/gym-minigrid), [OpenAI Gym](https://gym.openai.com/)|[PointNav](https://arxiv.org/pdf/1807.06757.pdf), [ObjectNav](https://arxiv.org/pdf/2006.13171.pdf), [MiniGrid tasks](https://github.com/maximecb/gym-minigrid), [Gym Box2D tasks](https://gym.openai.com/envs/#box2d)|[A2C](https://arxiv.org/pdf/1611.05763.pdf), [PPO](https://arxiv.org/pdf/1707.06347.pdf), [DD-PPO](https://arxiv.org/pdf/1911.00357.pdf), [DAgger](https://www.ri.cmu.edu/pub_files/2011/4/Ross-AISTATS11-NoRegret.pdf), Off-policy Imitation|
## Contributions
We welcome contributions from the greater community. If you would like to make such a contributions we recommend first submitting an [issue](https://github.com/allenai/allenact/issues) describing your proposed improvement. Doing so can ensure we can validate your suggestions before you spend a great deal of time upon them. Improvements and bug fixes should be made via a pull request from your fork of the repository at [https://github.com/allenai/allenact](https://github.com/allenai/allenact).
All code in this repository is subject to formatting, documentation, and type-annotation guidelines. For more details, please see the our [contribution guidelines](CONTRIBUTING.md).
## Acknowledgments
This work builds upon the [pytorch-a2c-ppo-acktr](https://github.com/ikostrikov/pytorch-a2c-ppo-acktr-gail) library of Ilya Kostrikov and uses some data structures from FAIR's [habitat-lab](https://github.com/facebookresearch/habitat-lab). We would like to thank Dustin Schwenk for his help for the public release of the framework.
## License
AllenAct is MIT licensed, as found in the [LICENSE](LICENSE) file.
## Team
AllenAct is an open-source project built by members of the PRIOR research group at the Allen Institute for Artificial Intelligence (AI2).
## Citation
If you use this work, please cite our [paper](https://arxiv.org/abs/2008.12760):
```bibtex
@article{AllenAct,
author = {Luca Weihs and Jordi Salvador and Klemen Kotar and Unnat Jain and Kuo-Hao Zeng and Roozbeh Mottaghi and Aniruddha Kembhavi},
title = {AllenAct: A Framework for Embodied AI Research},
year = {2020},
journal = {arXiv preprint arXiv:2008.12760},
}
```