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CoRL |
This repository and corresponding documentation site are currently under construction. We are still porting items and updating instructions for GitHub.
The Core ACT3 Reinforcement Learning library (CoRL) is created and maintained by the Air Force Research Laboratory’s (AFRL) Autonomy Capability Team (ACT3). CoRL is intended to enable scalable deep reinforcement learning (RL) experimentation in a manner extensible to new simulations and new ways for the learning agents to interact with them. The objective is to make RL research easier by removing lock-in to particular simulations.
- Makes RL environment development significantly easier
- Provides hyper configurable environments, agents and experiments
- Record observations by adding a few lines of config (instead of creating a new file for each observation)
- Reuse glues/dones/rewards between different tasks if they are general
- Uses an episode parameter provider (EPP) to randomize both domain and curriculum learning
- Has an integration first focus, which means that integrating agents to the real world or different simulators is significantly easier
- CoRL: Environment Creation and Management Focused on System Integration
- Inside the special F-16 the Air Force is using to test out ML
- AFRL, AFTC collaborate on future technology via weeklong autonomy summit
- Demonstrating and testing machine learning applications in aerospace
Documentation for the CoRL repository can be accessed directly as files in this repository, as a public documentation site, or can be built locally as an MkDocs site.
The full public documentation site is available on GitHub pages.
A local version of the documentation site can be built using MkDocs.
Build the documentation:
mkdocs build
Follow CLI prompts, as needed, to install all required plugins.
Serve the documentation:
mkdocs serve
Initial contributors include scientists and engineers associated with the Air Force Research Laboratory (AFRL), Autonomy Capability Team 3 (ACT3), and the Aerospace Systems Directorate (RQ).
If you use CoRL in your work, please use the following BibTeX to cite the CoRL white paper:
@inproceedings{
title={CoRL: Environment Creation and Management Focused on System Integration},
author={Justin D. Merrick, Benjamin K. Heiner, Cameron Long, Brian Stieber, Steve Fierro, Vardaan Gangal, Madison Blake, Joshua Blackburn},
year={2023},
url={https://arxiv.org/abs/2303.02182}
}
To cite the source code, use the Cite this repository option on GitHub to access the reference.
Approved for public release: distribution unlimited.
Date | Release Number | Description |
---|---|---|
2022-05-20 | AFRL-2022-2455 | Initial release |
2023-03-02 | APRS-RYZ-2023-01-00006 | Second release |
2024-21-03 | AFRL-2024-1562 | Third release |
- Controlled by: Air Force Research Laboratory (AFRL)
- Controlled by: AFRL Autonomy Capability Team (ACT3)
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