Skip to content

A practice implementation of reinforcement learning with PARL framework

Notifications You must be signed in to change notification settings

hl2500/Flappy_Bird

Repository files navigation

Flappy_Bird - a practice implementation of reinforcement learning with PARL framework

Information

This is a practice project of reinforcement learning. A Deep Q-Network (DQN) model was trained with PARL framework to play the game Flappy Bird. Modification of the parameter settings or longer training time may be necessary to obtain a better score.

Put all files under the same directory.
Train and evaluation:
python train.py
Test and display:
python test.py

test

Requirements

  1. paddlepaddle: https://github.com/PaddlePaddle/Paddle
  2. PARL: https://github.com/PaddlePaddle/PARL
  3. PyGame-Learning-Environment: https://github.com/ntasfi/PyGame-Learning-Environment

References

  1. https://github.com/PaddlePaddle/PARL/tree/develop/examples
  2. https://github.com/ninetailskim/FlappyPaddle
  3. https://github.com/Ryan906k9/fly_bird_RL

About

A practice implementation of reinforcement learning with PARL framework

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages