GTCOARLab
an environment for interactive exploration of reinforcement learning
| Build | Interactive Demo |
|---|---|
 |
[ ][demo] |
![a screenshot of GTCOARLab running gym-atari on mybinder.org][screenshot]
Downloading GTCOARLab
- View the releases on GitHub
- Choose the right artifacts for your operating system and hardware
- CPU: does not require a CUDA-capable graphics card and drivers
- GPU: some features require a CUDA-capable graphics card and drivers
- Download the artifacts (they are big, it will take a while)
- If your platform contains multiple
.zipand.z01,.z02files, you need to download them all- a GUI or the
zipcommand line tool will know how to decompress them correctly by using the.zipfile, which should be smaller than the others
- a GUI or the
- If your platform contains multiple
- Follow the instructions for e.g. [miniconda][miniconda-install]
- on Windows, prefer installing to a short path on a fast device (e.g. SSD) such
as
C:/gtcl - if you've already installed Anaconda, Miniconda, or Miniforge, it is recommended to not enable shell integration, environment variables, or registering as the default Python
- on Windows, prefer installing to a short path on a fast device (e.g. SSD) such
as
- From the command line, activate the environment
- on Windows, you should see a Start Menu entry
- run
jupyter lab, and you should see the JupyterLab interface open in your default browser with - to choose your browser, start
jupyter lab --no-browserand copy/paste the URL shown into your browser of choice
- If you run into problems, create an issue on GitHub
[demo]: https://mybinder.org/v2/gh/gt-coar/gt-coar-lab/master?urlpath=lab/notebooks/Headless%20Gym.ipynb [screenshot]: https://user-images.githubusercontent.com/7581399/111483945-ab1b1180-870b-11eb-8ce3-2bd81b205733.png
[miniconda-install]: https://conda.io/projects/conda/en/latest/user-guide/install/index.html
Copyright (c) 2021 University System of Georgia and GTCOARLab Contributors
Distributed under the terms of the BSD-3-Clause License