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A toolbox for worst-case validation of autonomous policies

Project description

Adaptive Stress Testing Toolbox


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A toolbox for worst-case validation of autonomous policies.

Adaptive Stress Testing is a worst-case validation method for autonomous policies. This toolbox is being actively developed by the Stanford Intelligent Systems Lab.

See for documentation.

Maintained by the Stanford Intelligent Systems Lab (SISL)

  • Free software: MIT license


Pip Installation Method

You can install the latest stable release from pypi:

pip install ast-toolbox

You can also install the latest version with:

pip install git+

Using the Go-Explore work requires having a Berkely DB installation findable on your system. If you are on Linux:

sudo apt-get update
sudo apt install libdb-dev python3-bsddb3

If you are on OSX:

brew install berkeley-db
export BERKELEYDB_DIR=$(brew --cellar)/berkeley-db/5.3

Once you have the Berkeley DB system dependency met, you can install the toolbox:

pip install ast-toolbox[ge]

Git Installation Method

If you are interested in development, you should clone the repo. You can use https:

git clone

You can also use ssh:

git clone

If you are on Linux, use the following commands to setup the Toolbox:

cd AdaptiveStressTestingToolbox
git submodule update --init --recursive
sudo chmod a+x scripts/
sudo scripts/
source scripts/


You can find our documentation on readthedocs.


Please see our Contributions Guide.


Built using the cookiecutter-pylibrary by Ionel Cristian Mărieș


2020.06.01.dev1 (2020-05-17)

  • First release on PyPI.

2020.09.01.dev1 (2020-09-01)

  • Update documentation.

  • Added docstrings and full apidocs.

  • Fix for Backward Algorithm.

  • Separate the toy AV simulator from the AST wrapper.

  • Change AST environments to save the cloned sim state from pre-action, not post-action.

  • Add travis deployment to PyPI.

  • Removal of unsupported files.

  • Expanded codecov to 90+%.

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