This repository contains the source code and data for the experiments presented in Deep Reinforcement Learning for Active Wake Control (to be published at AAMAS'22).

The current version of the repository contains only the environment itself, so that users do not have to download the experiments presented in the paper if they do not need them.

If you are interested in the scripts and configurations for the experiments or the supplementary material for the original paper, please check the release 0.0.1 of this repository for the code and the instructions.

If you are interested in using the wind farm environment with your own reinforcement learning agents, you can build and install a Python package that will make the environment available on your machine.

First, make sure that you have build installed by running python3 -m pip install --upgrade build.

Next, build the package by running python3 -m build from the project's directory. For linux based systems you can be asked to install python3-venv (i.e. apt install python3-venv).

This will create ./dist with a wind_farm_gym-<VERSION>-py3-none-any.whl file in it, where <VERSION> is the current release version.

Finally, install the package by running python3 -m pip install dist/wind_farm_gym-<VERSION>-py3-none-any.whl, or python3 -m pip install dist/wind_farm_gym-<VERSION>-py3-none-any.whl --force-reinstall if you want to reinstall an existing installation. This should install the package and its dependencies.

To test that the package is available and working, you can run the following example, also available at build-test.py. A simulation window should appear on your screen showing an overhead view of a three-turbine wind farm.

from wind_farm_gym import WindFarmEnv

# Initialize the environment with 3 turbines positioned 750 meters apart in a line
env = WindFarmEnv(turbine_layout=([0, 750, 1500], [0, 0, 0]))

obs = env.reset()
for _ in range(1000):                # Repeat for 1000 steps
    a = env.action_space.sample()    # Choose an action randomly
    obs, reward, _, _ = env.step(a)  # Perform the action
    env.render()                     # Render the environment; remove this line to speed up the process
env.close()

Please, cite the paper if you use it:

@inproceedings{Neustroev2022,
  title     = {Deep Reinforcement Learning for Active Wake Control},
  author    = {Neustroev, Grigory and Andringa, Sytze P.E. and Verzijlbergh, Remco A. and de~Weerdt, Mathijs M.},
  booktitle = {International Conference on Autonomous Agents and Multi-Agent Systems},
  year      = {2022},
  address   = {Online},
  publisher = {IFAAMAS},
  month     = {May},
  numpages  = {10}
}