Training a Neural Network to play Subway Surfers using Computer Vision

Using computer vision and a convolutional neural network (CNN) a model was trained to play the game Subway Surfers. It learned from my own playing, through which I have created and augmented a dataset from which it could induce the best moves to make based on what was happening in the game in real-time.

Introduction

My fascination for Machine Learning and it’s capabilities sparked with famous breakthrough-projects, where machines learned to play various (video-)games. Watching AlphaGo defeat Lee Sedol was when I first saw, how algorithms could master a complex game with endless possibilities like Go or Chess and even learn something like intuition. Projects like DeepMind’s AlphaStar pushed the boundaries of AI even further by teaching machines to strategize, adapt, and even collaborate. These breakthroughs inspired me to make my own attempts of using ML to train neural networks to play games.

Even though I was still a rookie in terms of machine learning back when I started this project in 2021 and the code isn’t great, I am still very proud of my achievement. So here is a breakdown of how I trained a neural network to play the game Subway Surfers in real-time using a convolutional neural network (CNN) and computer vision. Also watch my YouTube-Video explaining this approach, if you like.

Breakdown

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Training a Neural Network to play Subway Surfers using Computer Vision

Introduction

Breakdown

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