Source code and data for the experiments presented in Deep Reinforcement Learning for Active Wake Control
The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
Datacite citation style:
Neustroev, Greg; de Weerdt, Mathijs; Remco Verzijbergh; Sytze Andringa (2022): Source code and data for the experiments presented in Deep Reinforcement Learning for Active Wake Control. Version 1. 4TU.ResearchData. software. https://doi.org/10.4121/19107257.v1Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
This is a simulation study to illustrate benefits of reinforcement learning (RL) for active wake control in wind farms. The repository includes a simulator (./code/wind_farm_gym), implementation of RL agents (./code/agent), and configurations for the experiments presented in the paper (./code/configs), as well as the simulation results (./data). For more detailed instructions, see README.md.
- 2022-02-04 first online, published, posted
organizationsTU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science, Department of Software Technology