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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
We study the performance of various deep reinforcement learning algorithms for the problem of microgrid’s energy management system. We propose a novel microgrid model that consists of a wind turbine generator, an energy storage system, a population of thermostatically controlled loads, a population of price-responsive loads, and a connection to the main grid. The proposed energy management system is designed to coordinate between the different sources of flexibility by defining the priority resources, the direct demand control signals and the electricity prices. Seven deep reinforcement learning algorithms are implemented and empirically compared in this paper. The numerical results show a significant difference between the different deep reinforcement learning algorithms in their ability to converge to optimal policies. By adding an experience replay and a second semi-deterministic training phase to the well-known Asynchronous advantage actor critic algorithm, we achieved considerably better performance and converged to superior policies in terms of energy efficiency and economic value.
| Date | Stars |
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| 2026-07-31 | 251 |
| 2026-08-02 | 251 |
| 2026-08-03 | 251 |
| 2026-08-06 | 251 |
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# Deep Reinforcement Learning for Microgrid Energy Management 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. ## Installation Clone the repository and navigate to the directory <br> Create a conda environment <br> <code> conda env create -f conda.yaml </code> <br> Activate the environment <br> <code> conda activate tf2-gpu </code> ## Usage To train the DRL agent, you can use the A3C_plusplus.py file. <br> <code> python A3C_plusplus.py --train </code> <br> To evaluate the performance of a trained model, you can use the same file with the option --test. <br> <code> python A3C_plusplus.py --test </code> <br> ## Contributing Contributions to this repository are welcome! If you find a bug or have an idea for an improvement, please submit a pull request.<br> ## License 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
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:1e8ab5f3055e2264, llm:Repository description: compares seven deep reinforcement learning algorithms for microgrid energy management; proposes microgrid model and implements DRL algorithms.
matched fp:1e8ab5f3055e2264, llm:Repository description: compares seven deep reinforcement learning algorithms for microgrid energy management; proposes microgrid model and implements DRL algorithms.