ast-toolbox 2020.9.1.7

Description:

asttoolbox 2020.9.1.7

Adaptive Stress Testing Toolbox
v2020.09.01.7.



Overview
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 https://ast-toolbox.readthedocs.io/en/latest/ for documentation.
Maintained by the Stanford Intelligent Systems Lab (SISL)

Free software: MIT license


Installation

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+https://github.com/sisl/AdaptiveStressTestingToolbox.git@master
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
export YES_I_HAVE_THE_RIGHT_TO_USE_THIS_BERKELEY_DB_VERSION=1
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 https://github.com/sisl/AdaptiveStressTestingToolbox.git
You can also use ssh:
git clone [email protected]:sisl/AdaptiveStressTestingToolbox.git
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/install_all.sh
sudo scripts/install_all.sh
source scripts/setup.sh



Documentation
You can find our documentation on readthedocs.


Development
Please see our Contributions Guide.


Acknowledgements
Built using the cookiecutter-pylibrary by Ionel Cristian Mărieș


Changelog

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+%.