Wildfire RL is a reinforcement learning project for wildfire suppression agents, built using PyTorch and free-range-zoo.
This project was developed for the MOASEI 2025 Competition (part of the AAMAS 2025 Competitions), and was awarded as the winner of Track #3 (Wildfire).
- Hossein Savari
- Ali Jahani
- Afsaneh Habibi
Note: Training was done by using a much larger parallel-envs variable.
You can download pre-trained models from this link.
python main.pypython WildfireEvaluation.py --model-to-load 180 --testing_episodes 500 --seed 1 evalout "path/to/WS1.pkl"-
Add a proper license
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Write a complete installation guide
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Add argparse with helpful CLI descriptions
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Refactor conv_agent module
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Clean up training logic
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Add proper storage for experience replay (and other configurations)
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Add experimentations for individual predictors
Copyright (C) 2025 Ali Jahani, Hossein Savari, Afsaneh Habibi
This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this program; if not, see https://www.gnu.org/licenses.