|Project Name||Stars||Downloads||Repos Using This||Packages Using This||Most Recent Commit||Total Releases||Latest Release||Open Issues||License||Language|
|Drl Flappybird||536||3 years ago||5||Python|
|Playing Flappy Bird Using Deep Reinforcement Learning (Based on Deep Q Learning DQN using Tensorflow)|
|Reinforcement Learning Algorithms||407||2 years ago||4||Python|
|This repository contains most of pytorch implementation based classic deep reinforcement learning algorithms, including - DQN, DDQN, Dueling Network, DDPG, SAC, A2C, PPO, TRPO. (More algorithms are still in progress)|
|Flappy Bird Deep Q Learning Pytorch||407||2 years ago||4||mit||Python|
|Deep Q-learning for playing flappy bird game|
|Deeplearningforfun||335||9 months ago||mit||Scala|
|Implementation of some interesting ideas of deeplearning.|
|Use deep learning to auto play flappy bird|
|Flappybird Es||31||6 years ago||1||Python|
|An AI agent Learning to play Flappy Bird using Evolution Strategies and deep learning models.|
|Playing Custom Games Using Deep Learning||22||5 years ago||Python|
|Implementation of Google's paper on playing atari games using deep learning in python.|
|Neuroevolution Flappy Bird||15||5 years ago||mit||Jupyter Notebook|
|A comparison between humans, neuroevolution and multilayer perceptrons playing Flapy Bird implemented in Python|
|Flappybird Nueralnetwork||4||6 years ago||Python|
|Using Deep Q Network to play Flappy Bird|
See our video demo on YouTube Video Demo
Want to train a Flappy Bird?
Deep Q Learning algorithm is originally described in Playing Atari with Deep Reinforcement Learning, a paper from NIPS 2013 Deep Learning Workshop from DeepMind.
Our implementation follows Deep Q Learning Demo
Our goal is to train a deep neural network with q-learning technique to learn control policy from inputs generated by the game. Over time, the flappy bird learns to flap or not at a point to avoid as many pipes as possible.