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Master Thesis + Paper: Advancing Forest Fire Prevention: Deep Reinforcement Learning for Effective Firebreak Placement

This repository contains the code and resources for the master thesis titled "Advancing Forest Fire Prevention: Deep Reinforcement Learning for Effective Firebreak Placement".

Overview

The objective of this thesis is to explore the application of reinforcement learning techniques to optimize the allocation of firebreaks in forest management. The study utilizes the Cell2Fire simulator to model and simulate forest fire scenarios.

Paper

The findings and methodologies of this research are detailed in the paper: Advancing Forest Fire Prevention: Deep Reinforcement Learning for Effective Firebreak Placement.

Requirements

  • Required Python packages are listed in requirements.txt

Installation

  1. Clone the repository: 
    git clone https://github.com/yourusername/your-repo.git
  2. Navigate to the project directory: 
    cd your-repo
  3. Install the required packages: 
    pip install -r requirements.txt

Contact

For any questions or inquiries, please contact lucasmurrayh at gmail dot com.

License

This project is licensed under the MIT License. See the LICENSE file for details.