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An open source framework for research in Embodied AI

License: MIT Documentation Status Latest Release Python 3.6 LGTM Grade: Python Code style: 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). AI2 is a non-profit institute with the mission to contribute to humanity through high-impact AI research and engineering.

Features & Highlights#

  • Support for multiple environments: Support for the iTHOR, RoboTHOR and Habitat embodied environments as well as for grid-worlds including 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, DD-PPO, A2C, Imitation Learning and DAgger 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, RoboTHOR, Habitat, MiniGrid, OpenAI Gym PointNav, ObjectNav, MiniGrid tasks, Gym Box2D tasks A2C, PPO, DD-PPO, DAgger, Off-policy Imitation


We welcome contributions from the greater community. If you would like to make such a contributions we recommend first submitting an issue 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

All code in this repository is subject to formatting, documentation, and type-annotation guidelines. For more details, please see the our contribution guidelines.


This work builds upon the pytorch-a2c-ppo-acktr library of Ilya Kostrikov and uses some data structures from FAIR's habitat-lab. We would like to thank Dustin Schwenk for his help for the public release of the framework.


AllenAct is MIT licensed, as found in the LICENSE file.


AllenAct is an open-source project built by members of the PRIOR research group at the Allen Institute for Artificial Intelligence (AI2).


If you use this work, please cite our paper:

  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},