Flappy bird automation using Neuroevolution of Augmenting Topologies (NEAT) in Python

Related tags

Deep Learning python machine-learning genetic-algorithm flappy-bird unsupervised-learning neuroevolutionofaugmentedtopologies
Overview

FlappyAI

Flappy bird automation using Neuroevolution of Augmenting Topologies (NEAT) in Python

Everything Used

  • Genetic Algorithm especially NEAT concept
  • Unsupervised Learning
  • Neural Network
  • NEAT-Python used in developing the Genetic Algorithm (NEAT) and also the Neural Network (Forward Propagation)
  • Matplotlib and Pillow used in the visualization of the neural network
  • Pygame used for creating the game (Environment)

Files Documentation

  • Bin (All of the python scripts are here)
    • environment.py > Helper class that control the game itself (Rendered, Pipe, Bird, Gravity, and also game speed)
    • evolve.py > Genetic Algorithm for generating the best individual
    • main.py > Using the generated best individual from evolve.py and then put the individual to the game alone
    • visualize.py > Helper class that visualize the neural network in another window
  • Img (Assets that is used by the game)
  • Model (Where the best individuals are stored)

Resource

Installation

In case you want to try it on your local machine

  1. Clone
  2. Enter the virtual env
    • in windows powershell you can
    cd Scripts
./activate
  3. And now you can run the scripts inside /bin
  • You don't need to install the requirements inside requirements.txt when you use the virtual env

Notes

  • In the main.py, default best bird is still hard coded (I think I just deleted the .pickle files but still manage to stored those value, you can customize and make your own bird farm)
  • Using the above hard coded sample, I've never seen the bird fail
  • Game speed, visualization of the neural network can be customized in main.py hyperparam
  • Feel free to reach me in discord
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Comments
  • CVE-2007-4559 Patch

    CVE-2007-4559 Patch

    Patching CVE-2007-4559

    Hi, we are security researchers from the Advanced Research Center at Trellix. We have began a campaign to patch a widespread bug named CVE-2007-4559. CVE-2007-4559 is a 15 year old bug in the Python tarfile package. By using extract() or extractall() on a tarfile object without sanitizing input, a maliciously crafted .tar file could perform a directory path traversal attack. We found at least one unsantized extractall() in your codebase and are providing a patch for you via pull request. The patch essentially checks to see if all tarfile members will be extracted safely and throws an exception otherwise. We encourage you to use this patch or your own solution to secure against CVE-2007-4559. Further technical information about the vulnerability can be found in this blog.

    If you have further questions you may contact us through this projects lead researcher Kasimir Schulz.

    opened by TrellixVulnTeam 0
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Eryawan Presma Y.
Eryawan Presma Y.
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