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

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
Owner
Eryawan Presma Y.
Eryawan Presma Y.
Speckle-free Holography with Partially Coherent Light Sources and Camera-in-the-loop Calibration

Speckle-free Holography with Partially Coherent Light Sources and Camera-in-the-loop Calibration Project Page | Paper Yifan Peng*, Suyeon Choi*, Jongh

Stanford Computational Imaging Lab 19 Dec 11, 2022
A Vision Transformer approach that uses concatenated query and reference images to learn the relationship between query and reference images directly.

A Vision Transformer approach that uses concatenated query and reference images to learn the relationship between query and reference images directly.

24 Dec 13, 2022
Locally cache assets that are normally streamed in POPULATION: ONE

Population One Localizer This is no longer needed as of the build shipped on 03/03/22, thank you bigbox :) Locally cache assets that are normally stre

Ahman Woods 2 Mar 04, 2022
This repository is for the preprint "A generative nonparametric Bayesian model for whole genomes"

BEAR Overview This repository contains code associated with the preprint A generative nonparametric Bayesian model for whole genomes (2021), which pro

Debora Marks Lab 10 Sep 18, 2022
DNA sequence classification by Deep Neural Network

DNA sequence classification by Deep Neural Network: Project Overview worked on the DNA sequence classification problem where the input is the DNA sequ

Mohammed Jawwadul Islam Fida 0 Aug 02, 2022
Image-retrieval-baseline - MUGE Multimodal Retrieval Baseline

MUGE Multimodal Retrieval Baseline This repo is implemented based on the open_cl

47 Dec 16, 2022
Cancer-and-Tumor-Detection-Using-Inception-model - In this repo i am gonna show you how i did cancer/tumor detection in lungs using deep neural networks, specifically here the Inception model by google.

Cancer-and-Tumor-Detection-Using-Inception-model In this repo i am gonna show you how i did cancer/tumor detection in lungs using deep neural networks

Deepak Nandwani 1 Jan 01, 2022
DTCN SMP Challenge - Sequential prediction learning framework and algorithm

DTCN This is the implementation of our paper "Sequential Prediction of Social Me

Bobby 2 Jan 24, 2022
A very simple tool to rewrite parameters such as attributes and constants for OPs in ONNX models. Simple Attribute and Constant Modifier for ONNX.

sam4onnx A very simple tool to rewrite parameters such as attributes and constants for OPs in ONNX models. Simple Attribute and Constant Modifier for

Katsuya Hyodo 6 May 15, 2022
TargetAllDomainObjects - A python wrapper to run a command on against all users/computers/DCs of a Windows Domain

TargetAllDomainObjects A python wrapper to run a command on against all users/co

Podalirius 19 Dec 13, 2022
This is the official PyTorch implementation for "Mesa: A Memory-saving Training Framework for Transformers".

Mesa: A Memory-saving Training Framework for Transformers This is the official PyTorch implementation for Mesa: A Memory-saving Training Framework for

Zhuang AI Group 105 Dec 06, 2022
Compact Bilinear Pooling for PyTorch

Compact Bilinear Pooling for PyTorch. This repository has a pure Python implementation of Compact Bilinear Pooling and Count Sketch for PyTorch. This

Grégoire Payen de La Garanderie 234 Dec 07, 2022
Semantic Segmentation Suite in TensorFlow

Semantic Segmentation Suite in TensorFlow. Implement, train, and test new Semantic Segmentation models easily!

George Seif 2.5k Jan 06, 2023
Computationally efficient algorithm that identifies boundary points of a point cloud.

BoundaryTest Included are MATLAB and Python packages, each of which implement efficient algorithms for boundary detection and normal vector estimation

6 Dec 09, 2022
Neural Turing Machines (NTM) - PyTorch Implementation

PyTorch Neural Turing Machine (NTM) PyTorch implementation of Neural Turing Machines (NTM). An NTM is a memory augumented neural network (attached to

Guy Zana 519 Dec 21, 2022
Code for our paper A Transformer-Based Feature Segmentation and Region Alignment Method For UAV-View Geo-Localization,

FSRA This repository contains the dataset link and the code for our paper A Transformer-Based Feature Segmentation and Region Alignment Method For UAV

Dmmm 32 Dec 18, 2022
Depth-Aware Video Frame Interpolation (CVPR 2019)

DAIN (Depth-Aware Video Frame Interpolation) Project | Paper Wenbo Bao, Wei-Sheng Lai, Chao Ma, Xiaoyun Zhang, Zhiyong Gao, and Ming-Hsuan Yang IEEE C

Wenbo Bao 7.7k Dec 31, 2022
In this work, we will implement some basic but important algorithm of machine learning step by step.

WoRkS continued English 中文 Français Probability Density Estimation-Non-Parametric Methods(概率密度估计-非参数方法) 1. Kernel / k-Nearest Neighborhood Density Est

liziyu0104 1 Dec 30, 2021
Official Pytorch implementation of 'RoI Tanh-polar Transformer Network for Face Parsing in the Wild.'

Official Pytorch implementation of 'RoI Tanh-polar Transformer Network for Face Parsing in the Wild.'

Jie Shen 125 Jan 08, 2023
Rank1 Conversation Emotion Detection Task

Rank1-Conversation_Emotion_Detection_Task accuracy macro-f1 recall 0.826 0.7544 0.719 基于预训练模型和时序预测模型的对话情感探测任务 1 摘要 针对对话情感探测任务,本文将其分为文本分类和时间序列预测两个子任务,分

Yuchen Han 2 Nov 28, 2021