%0 Computer Program
%A Neustroev, Greg
%A de Weerdt, Mathijs
%A Verzijbergh, Remco
%A Andringa, Sytze
%D 2022
%T Source code and data for the experiments presented in Deep Reinforcement Learning for Active Wake Control
%U https://data.4tu.nl/articles/software/Source_code_and_data_for_the_experiments_presented_in_Deep_Reinforcement_Learning_for_Active_Wake_Control/19107257/1
%R 10.4121/19107257.v1
%K reinforcement learning
%K active wake control
%K deep learning
%X 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.<br>
%I 4TU.ResearchData