How to Install DACBench

This is a guide on how to install DACBench and its benchmarks. Alternatively, you can also use pre-built containers containers.

First clone our GitHub repository:

git clone https://github.com/automl/DACBench.git
cd DACBench
git submodule update --init --recursive

We recommend installing within a virtual environment:

conda create -n dacbench python=3.6
conda activate dacbench

Now install DACBench with:

pip install -e .

To also install all dependecies used in the examples, instead run:

pip install -e .[example]

You should now have DACBench installed in the base version. This includes on the artificial benchmarks, all others have separate installation dependencies. The full list of options is:

  • cma - installs the PyCMA step size control benchmark

  • modea - installs the ModEA benchmark

  • modcma - installs the IOHProfiler versions of CMA step size and CMA algorithm control

  • sgd - installs the SGD benchmark

  • theory - installs the theory benchmark

  • all - installs all benchmark dependencies

  • example - installs example dependencies

  • docs - installs documentation dependencies

  • dev - installs dev dependencies

Please not that in order to use the FastDownward benchmarks, you don’t have to select different dependencies, but you have to build the planner. We recommend using cmake 3.10.2 for this:

./dacbench/envs/rl-plan/fast-downward/build.py

In the top level directory, you will find folders for tests, examples, code coverage reporting and documentation. The code itself can be found in the ‘dacbench’ folder. If you want to take advantage of our pre-run static and random baselines (10 runs each with 1000 episodes), you can download them here.