This project is focused on creating a dynamic escape route finder using the A* algorithm and Q-learning. The primary objective is to assist in fire evacuation scenarios by intelligently determining optimal escape routes.
To run the program, execute the main.py file. The code has been developed using PyCharm, ensuring a smooth development and execution environment. However, please note that there might be issues with image imports, necessitating adjustment of image file locations if errors are encountered.
A-Star Algorithm: The project incorporates the A-Star algorithm, a popular pathfinding and graph traversal algorithm, to efficiently compute optimal escape routes in the event of a fire.
Q-learning: Leveraging reinforcement learning principles, Q-learning enhances the system's ability to adapt dynamically, continuously improving escape route decisions based on experience.
Execute the main.py file to initiate the escape route finder. Follow any on-screen instructions for input or customization, and observe the system's intelligent determination of the optimal escape path.
This project was developed using PyCharm, ensuring a streamlined coding and debugging experience. It is recommended to use a compatible IDE for optimal results.
Adjust the file paths for image imports if errors are encountered. Ensure that image files are correctly located to enable proper functioning of the program.
Contributions and feedback are welcome. Feel free to submit issues or suggestions to enhance the functionality and reliability of the escape route finder.