Quick-start

Installing CommonPower

You will need Python >= 3.8 installed on your system.

We recommend using a virtual environment to work with CommonPower. To create a virtual environment run

python -m venv </path/to/new/virtual/environment>

You can then activate the virtual environment.

Linux:

source <path/to/venv>/bin/activate

Windows:

<path/to/venv>\Scripts\activate.bat

You can then proceed to install CommonPower. For local development, install the library in editable mode:

cd <your/working/directory>
git clone "https://github.com/TUMcps/commonpower.git"
pip install -e <absolute/path/to/the/commonpower/directory>

Otherwise, install CommonPower via PyPI:

pip install commonpower

Multi-agent reinforcement learning

At the moment, CommonPower supports multi-agent reinforcement learning using the IPPO/MAPPO implementation detailed in this paper. Since we had to make a few adjustments, we forked the original repository. Please clone our fork, cd into the repository and install the package to your virtual environment using pip install -e ..

Gurobi

We use Gurobi as a default solver for our optimization problems. As a student, you can obtain an academic license. There are to options: If you want to run CommonPower on you laptop, you can use the named-user license. To run it on a server, you need the WLS license. After obtaining a license, follow the Gurobi quickstart guide (choose the appropriate one for your system) to install Gurobi and retrieve your license. If you use Gurobi on a server (with the WLS license) and receive the error that it requires two many cores, you can just submit a ticket with the error message and your WLS license will be upgraded.

Get started

Have a look at the Introduction tutorial to learn more about how CommonPower is structured.