Kendrick Tan

@kendricktan0814

Machine Learning Bot for Facebook Messenger Games

Github link to the project.

hw.

Facebook Messenger recently received an update that allows users to play games within the App, amongst them was a vertical scrolling game — Endless Lake, which was getting pretty competitive within my social group.

In this article I’ll be talking about the processes I used and challenges I faced whilst building a bot for the game Endless Lake.

endless lake

I don’t like having free time

I recently finished a side project and was feeling rather empty inside, and so I decided to build a simple bot to play this game with the following two rules enforced:

  1. The inputs for the robot must either be the raw pixel values from the screen or a processed version of the raw pixel values.
  2. No hard-coding rules. Or in other words, it’ll attempt to learn how to play the game by watching a user play it.

bot? bot.

Python was chosen as the language of choice as I was familiar with it and it has a ton of machine-learning libraries that integrates with it.

Mouse/Keyboard Event handler

This is really crucial in collecting data and enabling the bot to play the game. Fortunately there’s a really easy to use library which fits the bill — pyuserinput.

Screen Capture

Capturing a region of the screen at a good rate (at least 30 fps) was already a challenge due to Python’s interpretive nature. Well-known libraries such as pyscreenshot or PIL/Pillow was able to take a screenshot of the region every 0.1 seconds or so (or around 10 fps), which was too slow and only 1/3 of the speed which I originally wanted.

I was ready to give up until I stumbled onto this korean blog talking about python-wx. Installation was a pain in the ass but was worth it. I could capture a region of my screen at around 200 fps, a 20 fold increase!

If you would like to have fast screencapture using Python, have a look at the code.

Pre-processing

I decided to use OpenCV as it had a suite of tools that suited my needs. After grabbing the screen region, I used Otsu’s dynamic thresholding to figure out the contour of the game window, and crop it so it only captures the game window and nothing more.

I then divided the game window into a NxN grid (which can be changed), used some simple RGB thresholding to figure out where the platform and player was relative to each other.

Since I didn’t really care anything that was too far ahead or too far behind the player, I decided to just take into account 6 rows in front of the player. These rows will be used as inputs for our neural network. A visualization of the inputs can be seen below. (Red=Water, Green=Platform, Blue=Player)

Grid area depicts the inputs for our neural network

ML

I decided to use Neural Networks from scikit-learn due to its sheer simplicity and ease-of-use. Of course you can use other techniques such as random trees, SVMs, KNN, etc.

Does it even work?

Errr well, it does, with enough data.

Here’s how it performs with 1 training set:

6 training sets:

10 training sets:

TL;DR:

More data = more performance

If you have any questions just email me at kendricktan0814 at gmail.com.

Hacker Noon is how hackers start their afternoons. We’re a part of the @AMI family. We are now accepting submissions and happy to discuss advertising & sponsorship opportunities.
If you enjoyed this story, we recommend reading our latest tech stories and trending tech stories. Until next time, don’t take the realities of the world for granted!
Topics of interest

More Related Stories