Information
AllenAct Overview @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} , } Next 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. 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 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. 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: