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A small tool which can convert automatically synthesised formally verified controllers into concise decision trees.

Project description

dtControl: Decision Tree Learning Algorithms for Controller Representation

System requirements

To run dtControl, you need Python 3.8 or higher with several other libraries which are automatically installed if installing with the python-based pip package manager. We have tested the installation and basic functionality on Ubuntu Linux, MacOS Catalina and Windows 10.

Installing dtControl on your machine

For most users, running the following command should install the latest version of dtControl, as long as you have Python 3.8 or newer and pip installed on your system:

$ pip install dtcontrol

Note that in case you have both Python 2 as well as Python 3 installed, you might have to run python3 -m pip install dtcontrol.

Manual Installation

Note: In case of difficulty when following any of the instructions in this section, please check the section ‘Common Installation Issues’ below

  1. Make sure you have Python 3.8 (or newer), pip3 and python3-venv for creating virtual environments.

    On Ubuntu 16.10 or newer:

    $ sudo apt-get install python3 python3-pip python3-venv

    On MacOS, you can install with the help of the package manager Homebrew:

    $ brew install python3

    or refer to this tutorial if you don’t have Homebrew installed.

    On Windows, one may follow this or this tutorial.

  2. Use a virtual environment to make sure that the installation is clean and easy, and does not interfere with any other python packages installed in your system. Create a new folder dtcontrol and create a virtual environment inside it and activate the virtual environment:

    $ mkdir dtcontrol
    $ cd dtcontrol
    $ python3 -m venv venv
    $ source venv/bin/activate

    Run python and verify that the displayed version is greater than 3.8. Press Ctrl+D to exit the python console again.

  3. With the virtual environment activated, run:

    $ pip install dtcontrol

    This should install dtControl and all its dependencies. Try running dtControl by typing dtControl in the console. It should print the help text.

Uninstallation You can delete the dtcontrol folder created above to delete all traces of dtcontrol as well as its dependencies.

Common Installation Issues

  1. If sudo apt-get install python3.6 does not work, this askubuntu answer might help you.

  2. In case of errors when trying to run virtualenv, check that it is located in a directory that is included in your path; this stackoverflow answer might be relevant.

  3. If you don’t see what went wrong, leave the virtual environment (run “deactivate”), delete the folder rm -rf ~/dtcontrol-venv and go through all the installation steps again. If errors still occur, please raise an issue or contact us.

Running the experiments

This section assumes you have installed dtControl so that upon entering dtControl in your command line, the help text is displayed. Additionally it assumes that you have unzip-ed all examples in ./dtcontrol/examples. You can download dtControl examples and extract them into ./dtcontrol/examples or run the following from the terminal:

$ cd ./dtcontrol
$ git clone examples

Further, you may either manually unzip the specific case study you would like to run or use the following command to unzip all case studies at once:

$ find . -name "*.zip" | while read filename; do unzip -o -d "`dirname "$filename"`" "$filename"; done;

However, be warned that this would use up about 13GB of space.

Web-Based Graphical User Interface

In order to set up the web-based graphical user interface on a local machine, simply run the following command:

$ dtcontrol-frontend

If run successfully, this command should start the web interface of dtcontrol, which is now easily accessible via your favorite browser at

Command Line Interface

To execute a single algorithm on a single model via the command line, run a command like:

$ dtcontrol --input ./dtcontrol/examples/cartpole.scs --use-preset maxfreq --timeout 30m

If run successfully, this should create a benchmark.html file displaying the results of the current run. It should also create a decision_trees folder containing the output (DOT and C files) decision trees.

We have pre-defined a few preset methods, which can be listed using:

$ dtcontrol preset --list

Run dtcontrol preset --sample or see the manual for details on how to pick and mix your own presets.

Other commands can be found by running:

$ dtcontrol --help

Reading the output

To get an overview of the results, the file benchmark.html is created in the directory from which you call dtControl. You can open it in any browser.

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