This repository contains an implementation of a Deep Reinforcement Learning (DRL) algorithm for managing the energy demand and supply of a microgrid. The implementation is built using Python and is based on the OpenAI Gym environment.
Clone the repository and navigate to the directory
Create a conda environment
conda env create -f conda.yaml
Activate the environment
conda activate tf2-gpu
To train the DRL agent, you can use the A3C_plusplus.py file.
python A3C_plusplus.py --train
To evaluate the performance of a trained model, you can use the same file with the option --test.
python A3C_plusplus.py --test
Contributions to this repository are welcome! If you find a bug or have an idea for an improvement, please submit a pull request.
This code is released under the MIT License. More information about this project can be found at: https://doi.org/10.1016/j.segan.2020.100413