Currently, the sources of pypownet are mandatory to run the command line argument (if using the Docker image, they are already within the image).
If you installed pypownet using the Docker image, you will first need to launch bash within the image using:
sudo docker run -it --privileged --net=host --env="DISPLAY" --volume="$HOME/.Xauthority:/root/.Xauthority:rw" marvinler/pypownet sh
In any case, pypownet comes with a command-line interface that allows to run simulations with a specific agent and control parameters. The basic usage will run a do-nothing policy with the default parameters:
python -m pypownet.main
This basic usage will run a do-nothing agent on the default14/ parameters (emulates a grid with 14 substations, 5 productions, 11 consumptions and 20 lines), it takes ~100 seconds to run 1000 timesteps (old i5).
You can use
python -m pypownet.main --help for information about the CLI tool or check the CLI usage section
As package usage¶
The installation process should ensure that pypownet in installed along with your corresponding python3.6.
As a consequence, pypownet should be importable in your projects just like any package installed using pip:
Only the modules pypownet.environment, pypownet.agent and either pypownet.runner.Runner or pypownet.main should be used, as other packages are not supposed to be used out of the induced context.
Usually, what you would do is first create an instance of pypownet.environment.RunEnv with an environment parameters + crate an instance of pypownet.agent.Agent (or any subclass), then get an instance of
pypownet.runner.Runner with appropriate parameters (including previous
Agent), and run its
loop method to run the simulator.
The latter functions output the total reward at the end of the experiment.