mzy2240/l2rpn

CodaLab L2RPN: Learning to Run a Power Network

Learning to Run a Power Network

https://competitions.codalab.org/competitions/22845#learn_the_details-overview

Pypownet Installation

Requirements:

• Python >= 3.6
• Virtual Environment Recommended

cd L2RPN/
python setup.py install

Pypownet Introduction: https://github.com/MarvinLer/pypownet

Basic Usage

Runing Test

python Run_and_Submit_agent.py

You can modify the testing number of chronics and timesteps in the 'Run_and_Submit_agent.py' file.

Train Your Model

python -m pypownet.main -f train

To see all the options:

python -m pypownet.main --help

Key Files and Features

• data
  • Saved numpy files of action_space and generated train/val data
  • Trained model
• example_submission
  • Sample submission to the L2RPN competition
• parameters
  • reward_signal, configuration, and training chronics of different power grids
• public_data
  • Extra data for IEEE-14 bus
• pypownet
  • agent.py: Defines the Dueling DQN agent
  • analyze_action.py: Analyze the simulation results
  • generate_action_space.py: Generate action space
  • main.py: Main run file, including imitation, training, and test
  • prepare_data.py: prepare data for imitation learning
  • runner.py: key file controling the training process
• Run_and_Submit_agent.py
  • Test the trained model

License information

Copyright 2017-2019 GEIRINA, RTE, and INRIA (France)

GEIRINA: https://www.geirina.net/
RTE: http://www.rte-france.com
INRIA: https://www.inria.fr/

This Source Code is subject to the terms of the GNU Lesser General Public License v3.0. If a copy of the LGPL-v3 was not distributed with this file, You can obtain one at https://www.gnu.org/licenses/lgpl-3.0.fr.html.