Reinforcement Learning using the Actor-Critic framework for the L2RPN challenge (https://l2rpn.chalearn.org/ & https://competitions.codalab.org/competitions/22845#learn_the_details-overview). The agent trained using this code was one of the winners of the challenge. The code runs on the pypownet environment (https://github.com/MarvinLer/pypownet). It is released under a license of LGPLv3
Reinforcement Learning using the Actor-Critic framework for the L2RPN challenge (https://l2rpn.chalearn.org/ & https://competitions.codalab.org/competitions/22845#learn_the_details-overview). The agent trained using this code was one of the winners of the challenge. The code uses the pypownet environment (https://github.com/MarvinLer/pypownet). The code is released under a license of LGPLv3.
Pypownet Installation and Documentation : https://github.com/MarvinLer/pypownet
python pypow_14_a3c_final.py
This will create two new files
To speed up the learning, the enviornment difficulty level is slowly increased and the following hyper-parameters in the code can be used to make the environment difficult or easy
Copyright 2019 Amarsagar Reddy Ramapuram Matavalam
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.html.