Learn to play Flappy Bird game using Reinforcement Learning. For deep learning I have used keras for its simplicity. I am using Q-learning algorithm to achieve this. You can find paper here.
- Install tensorflow, keras, pygame and opencv
python flappy_reinforced.py==> You can either test using pretrained weights or choose to train from scratch
| Layer (type) | Output Shape | Param # |
|---|---|---|
| input_1 (InputLayer) | (None, 80, 80, 4) | 0 |
| conv2d_1 (Conv2D) | (None, 20, 20, 32) | 8224 |
| max_pooling2d_1 (MaxPooling2 | (None, 10, 10, 32) | 0 |
| conv2d_2 (Conv2D) | (None, 5, 5, 64) | 32832 |
| conv2d_3 (Conv2D) | (None, 5, 5, 64) | 4160 |
| flatten_1 (Flatten) | (None, 1600) | 0 |
| dense_1 (Dense) | (None, 512) | 819712 |
| dense_2 (Dense) | (None, 2) | 1026 |
- yenchenlin/DeepLearningFlappyBird for wrapped bird game of sourabhv/FlapPyBird and his wonderful documentation.
- Q-Learning Paper.