Demo video available at https://www.youtube.com/watch?v=PNE6KFNn1yY
After installing the requirements (NumPy, PyGame, and TensorFlow), run python train_nn.py -n to generate a new model. It will play 20 games of Flappy Bird, train, and do that again, retraining each round. If you have a saved model, run python train_nn.py -l and it will retrain on the model already available in the folder ./models.
Once the model has been trained, it can be tested with python test_flappy_results.py <model path> and it will play the game with the existing model.
Some pretrained models exist in ./saved_models
Anaconda (https://anaconda.org/anaconda/python), NumPy (conda install numpy), PyGame (pip install pygame), TensorFlow (conda install -c conda-forge tensorflow-gpu/conda install -c conda-forge tensorflow), Python 3.
Flappy bird implementation written in Python with PyGame from https://github.com/Max00355/FlappyBird, significantly modified to work with machine learning code.
python train_nn.py -n will generate a new model with random initial weights and keep training in iterations of 20 games. It will save a model in a new folder models/.
python test_flappy_results.py <model_name> will play Flappy Bird games with an initially trained model.
neural_jumper.py is a wrapper for the neural network to choose a jump.
tf_graph.py contains the neural network architecture.
config.py contains some configuration variables, such as whether to save images and directories.
global_vars.py contains some global variables - it should not be modified.