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align=\"center\"\u003e\n    \u003cimg src=\"docs/img/AllenAct.svg\" width=\"350\" /\u003e\n    \u003cbr\u003e\n    \u003ci\u003e\u003ch3\u003eAn open source framework for research in Embodied AI\u003c/h3\u003e\u003c/i\u003e\n    \u003c/p\u003e\n    \u003chr/\u003e\n\u003c/div\u003e\n\n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](./LICENSE)\n[![Documentation Status](https://img.shields.io/badge/docs-up%20to%20date-Green.svg)](https://allenact.org)\n[![Latest Release](https://img.shields.io/github/v/release/allenai/allenact)](https://github.com/allenai/allenact/releases/latest)\n[![Python 3.7](https://img.shields.io/badge/python-3.6+-blue.svg)](https://www.python.org/downloads/release/python-360/)\n[![LGTM Grade: Python](https://img.shields.io/lgtm/grade/python/g/allenai/allenact.svg?logo=lgtm\u0026logoWidth=18)](https://lgtm.com/projects/g/allenai/allenact/context:python)\n[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)\n\n**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.\n\nAllenAct 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.\n\n## Quick Links\n\n- [Website \u0026 Docs](https://www.allenact.org/)\n- [Github](https://github.com/allenai/allenact)\n- [Install](https://www.allenact.org/installation/installation-allenact/)\n- [Tutorials](https://www.allenact.org/tutorials/)\n- [AllenAct Paper](https://arxiv.org/abs/2008.12760)\n- [Citation](#citation)\n\n## Features \u0026 Highlights\n\n* _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).\n* _Task Abstraction_: Tasks and environments are decoupled in AllenAct, enabling researchers to easily implement a large variety of tasks in the same environment.\n* _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.\n* _Sequential Algorithms_: It is trivial to experiment with different sequences of training routines, which are often the key to successful policies.\n* _Simultaneous Losses_: Easily combine various losses while training models (e.g. use an external self-supervised loss while optimizing a PPO loss).\n* _Multi-agent support_: Support for multi-agent algorithms and tasks.\n* _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.\n* _Pre-trained models_: Code and models for a number of standard Embodied AI tasks.\n* _Tutorials_: Start-up code and extensive tutorials to help ramp up to Embodied AI.\n* _First-class PyTorch support_: One of the few RL frameworks to target PyTorch.\n* _Arbitrary action spaces_: Supporting both discrete and continuous actions.\n\n|Environments|Tasks|Algorithms|\n|------------|-----|----------|\n|[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|\n\n## Contributions\nWe 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).\n\nAll code in this repository is subject to formatting, documentation, and type-annotation guidelines. For more details, please see the our [contribution guidelines](CONTRIBUTING.md).\n\n## Acknowledgments\nThis 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.\n\n## License\nAllenAct is MIT licensed, as found in the [LICENSE](LICENSE) file.\n\n## Team\nAllenAct is an open-source project built by members of the PRIOR research group at the Allen Institute for Artificial Intelligence (AI2). \n\n\u003cdiv align=\"left\"\u003e\n    \u003ca href=\"//prior.allenai.org/\" target=\"_blank\"\u003e\n        \u003cimg src=\"docs/img/ai2-prior.svg\" width=\"400\"\u003e\n    \u003c/a\u003e\n    \u003cbr\u003e\n\u003c/div\u003e\n\n## Citation\nIf you use this work, please cite our [paper](https://arxiv.org/abs/2008.12760):\n\n```bibtex\n@article{AllenAct,\n  author = {Luca Weihs and Jordi Salvador and Klemen Kotar and Unnat Jain and Kuo-Hao Zeng and Roozbeh Mottaghi and Aniruddha Kembhavi},\n  title = {AllenAct: A Framework for Embodied AI Research},\n  year = {2020},\n  journal = {arXiv preprint arXiv:2008.12760},\n}\n```\n\n\n","funding_links":[],"categories":["Python"],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fallenai%2Fallenact","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fallenai%2Fallenact","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fallenai%2Fallenact/lists"}