Automating Clash of Clans attacks with YOLOv5

This article explores the use of YOLOv5, a deep learning model, to automate the Electro Dragon strategy in the mobile game Clash of Clans.

I used YOLOv5, a deep learning model, to automate the Electro Dragon strategy in the mobile game Clash of Clans. The project demonstrates an innovative application of machine learning, computer vision and automation in gaming.

Data Collection:

Since Clash of Clans isn’t a game environment with a state that is accessible. any tasks performed on the game would require computer vision and pyautogui. Which is why I first had to collect data. The data collection phase was a critical first step. Using PyAutoGUI, a Python library, I scripted a process to automatically capture screenshots of Clash of Clans gameplay. This script was programmed to take pictures every few seconds while cycling through various opponents' bases. The objective was to amass a wide-ranging dataset that represented as many base layouts as possible. This variety in data is crucial for training a robust machine learning model capable of recognizing diverse patterns and elements in the game.

Image Labeling:

Post-collection, the images were labeled manually using LabelImg. This process involved identifying and marking air defenses in each image, a key component in the electro dragon strategy.

Model Training:

Leveraging the power of YOLOv5, an advanced convolutional neural network, I trained a model on this labeled dataset. YOLOv5, known for its speed and accuracy, was ideal for real-time object detection. The training process involved fine-tuning various parameters to optimize the model’s ability to accurately identify and locate air defenses in the game. This step was a meticulous process, involving repeated testing and validation to ensure the model’s effectiveness and reliability.

Gameplay Automation:

With a trained and tested model, the final step was to integrate it into the gameplay using PyAutoGUI. This library enabled the automation of key game actions – targeting and zapping air defenses, followed by deploying dragons. The script was crafted to interpret the model's output in real-time and translate it into coordinated in-game actions, effectively playing the Electro Dragon strategy on its own.

Conclusion:

This project was not merely about automating a game strategy, it allowed me to dive into the world of machine learning and computer vision. Working with PyTorch and YOLOv5 provided a hands-on experience in object detection, data annotation, model training, and automation. This project shows AI applications in various fields, including gaming, and highlights the practical, real-world learning that can be achieved through such innovative projects.