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, e.g. using uv:
pip install uv
uv venv --python 3.10
source .venv/bin/activate
Now install DACBench with:
make install
To also install all dependecies used in the examples, instead run:
make install-dev
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 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.