Game Pathfinding Project

Overview

This project explores a pathfinding algorithm for a simulated game environment. The main goal of the project is to demonstrate the ability to apply programming and problem-solving skills to a defined context, simulating a real-world application.

Game Rules

The game is played on a 7x9 grid where a character starts in the middle of the field and has to complete exactly 15 steps without revisiting any cell or stepping out of the grid boundaries. The cells can be empty or contain prizes with different values (1, 2, or 3). The aim is to collect as many points as possible without reaching a dead end. If the path reaches a dead end (i.e., no further steps can be taken without breaking the rules), it is considered incomplete and non-viable. Specific Rules: The character starts at the center of the field. Each move can be up, down, left, or right. Stepping on an already visited cell is not allowed. The path must be exactly 15 steps long.

Code Description

This project includes several Python scripts encapsulated in a Jupyter Notebook that:

• Load and preprocess game field screenshots to categorize cells based on potential rewards using color analysis.
• Utilize a depth-first search (DFS) algorithm to explore all possible paths that collect prizes within the constraints of the game.
• Visualize the game field, the paths taken, and analyze the paths to determine the most and least effective strategies.

Goals

The main objectives of this project were:

• To gain hands-on experience with Python and libraries such as NumPy, Matplotlib, and PIL.
• To understand and implement a recursive pathfinding algorithm.
• To prectice data visualization skills by plotting the results of the algorithm.

Project Status

This project is currently in a prototype stage. It serves as a demonstration of my current skills and understanding in Python coding, algorithms, and data visualization. Further development may be undertaken to refine the code and expand its functionality.

Visual Demonstrations

Here are some images that demonstrate various aspects and outputs of the project:

Initial Field

Initial Field (Cropped)

Recognized and Visualized Field

Priority Path (3 Score)

Priority Path (2 Score)

Priority Path (1 Score)

Most Valuable Path

Least Valuable Path

Path with Least Empty Cells

Field Usage Visualization

Field Usage for Paths with Different Points Amount

Dependency of the Amount of Paths on Points Collected

Usage

To run this project, ensure you have Jupyter Notebook installed with Python 3, and the necessary libraries (NumPy, Matplotlib, and PIL). Open the notebook and execute the cells sequentially to see the pathfinding in action and visualize the results.