DeepLearningFlappyBird and RL-FlappyBird
These are competitors, as both repositories implement deep reinforcement learning, specifically Q-learning, to train an agent to play Flappy Bird independently, offering similar functionality with "yenchenlin/DeepLearningFlappyBird" being significantly more popular and mature.
About DeepLearningFlappyBird
yenchenlin/DeepLearningFlappyBird
Flappy Bird hack using Deep Reinforcement Learning (Deep Q-learning).
This project lets you observe how a computer program learns to play the game Flappy Bird by itself. It takes the game's screen pixels as input and, through trial and error, learns to make the bird flap at the right time to navigate through pipes. This is ideal for students, researchers, or enthusiasts curious about how artificial intelligence can learn complex tasks without explicit programming.
About RL-FlappyBird
kingyuluk/RL-FlappyBird
Using reinforcement learning to train FlappyBird.
This project allows developers to experiment with reinforcement learning by training an AI to play Flappy Bird. You provide the game environment and the learning algorithm, and the project outputs a trained model that can autonomously navigate the bird through obstacles. It's designed for machine learning practitioners interested in practical applications of Deep Q-Networks.
Scores updated daily from GitHub, PyPI, and npm data. How scores work