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