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.
A general python framework for visual object tracking and video object segmentation, based on PyTorch

PyTracking A general python framework for visual object tracking and video object segmentation, based on PyTorch. 📣 Two tracking/VOS papers accepted

2.6k Jan 04, 2023
The official codes of "Semi-supervised Models are Strong Unsupervised Domain Adaptation Learners".

SSL models are Strong UDA learners Introduction This is the official code of paper "Semi-supervised Models are Strong Unsupervised Domain Adaptation L

Yabin Zhang 26 Dec 26, 2022
Methods to get the probability of a changepoint in a time series.

Bayesian Changepoint Detection Methods to get the probability of a changepoint in a time series. Both online and offline methods are available. Read t

Johannes Kulick 554 Dec 30, 2022
DLWP: Deep Learning Weather Prediction

DLWP: Deep Learning Weather Prediction DLWP is a Python project containing data-

Kushal Shingote 3 Aug 14, 2022
Official implementation of paper "Query2Label: A Simple Transformer Way to Multi-Label Classification".

Introdunction This is the official implementation of the paper "Query2Label: A Simple Transformer Way to Multi-Label Classification". Abstract This pa

Shilong Liu 274 Dec 28, 2022
Cl datasets - PyTorch image dataloaders and utility functions to load datasets for supervised continual learning

Continual learning datasets Introduction This repository contains PyTorch image

berjaoui 5 Aug 28, 2022
[NeurIPS 2021] Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods

Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods Large Scale Learning on Non-Homophilous Graphs: New Benchmark

60 Jan 03, 2023
imbalanced-DL: Deep Imbalanced Learning in Python

imbalanced-DL: Deep Imbalanced Learning in Python Overview imbalanced-DL (imported as imbalanceddl) is a Python package designed to make deep imbalanc

NTUCSIE CLLab 19 Dec 28, 2022
Back to Basics: Efficient Network Compression via IMP

Back to Basics: Efficient Network Compression via IMP Authors: Max Zimmer, Christoph Spiegel, Sebastian Pokutta This repository contains the code to r

IOL Lab @ ZIB 1 Nov 19, 2021
A framework to train language models to learn invariant representations.

Invariant Language Modeling Implementation of the training for invariant language models. Motivation Modern pretrained language models are critical co

6 Nov 16, 2022
A PyTorch implementation of "ANEMONE: Graph Anomaly Detection with Multi-Scale Contrastive Learning", CIKM-21

ANEMONE A PyTorch implementation of "ANEMONE: Graph Anomaly Detection with Multi-Scale Contrastive Learning", CIKM-21 Dependencies python==3.6.1 dgl==

Graph Analysis & Deep Learning Laboratory, GRAND 30 Dec 14, 2022
A Simulation Environment to train Robots in Large Realistic Interactive Scenes

iGibson: A Simulation Environment to train Robots in Large Realistic Interactive Scenes iGibson is a simulation environment providing fast visual rend

Stanford Vision and Learning Lab 493 Jan 04, 2023
Supplementary materials for ISMIR 2021 LBD paper "Evaluation of Latent Space Disentanglement in the Presence of Interdependent Attributes"

Evaluation of Latent Space Disentanglement in the Presence of Interdependent Attributes Supplementary materials for ISMIR 2021 LBD submission: K. N. W

Karn Watcharasupat 2 Oct 25, 2021
Paddle pit - Rethinking Spatial Dimensions of Vision Transformers

基于Paddle实现PiT ——Rethinking Spatial Dimensions of Vision Transformers,arxiv 官方原版代

Hongtao Wen 4 Jan 15, 2022
Code accompanying the paper "ProxyFL: Decentralized Federated Learning through Proxy Model Sharing"

ProxyFL Code accompanying the paper "ProxyFL: Decentralized Federated Learning through Proxy Model Sharing" Authors: Shivam Kalra*, Junfeng Wen*, Jess

Layer6 Labs 14 Dec 06, 2022
Semi-Supervised Learning for Fine-Grained Classification

Semi-Supervised Learning for Fine-Grained Classification This repo contains the code of: A Realistic Evaluation of Semi-Supervised Learning for Fine-G

25 Nov 08, 2022
Official code of CVPR 2021's PLOP: Learning without Forgetting for Continual Semantic Segmentation

PLOP: Learning without Forgetting for Continual Semantic Segmentation This repository contains all of our code. It is a modified version of Cermelli e

Arthur Douillard 116 Dec 14, 2022
Generative Query Network (GQN) in PyTorch as described in "Neural Scene Representation and Rendering"

Update 2019/06/24: A model trained on 10% of the Shepard-Metzler dataset has been added, the following notebook explains the main features of this mod

Jesper Wohlert 313 Dec 27, 2022
Open source simulator for autonomous vehicles built on Unreal Engine / Unity, from Microsoft AI & Research

Welcome to AirSim AirSim is a simulator for drones, cars and more, built on Unreal Engine (we now also have an experimental Unity release). It is open

Microsoft 13.8k Jan 05, 2023
Line-level Handwritten Text Recognition (HTR) system implemented with TensorFlow.

Line-level Handwritten Text Recognition with TensorFlow This model is an extended version of the Simple HTR system implemented by @Harald Scheidl and

Hoàng Tùng Lâm (Linus) 72 May 07, 2022