## Playing Flappy Bird Using Deep Reinforcement Learning (Based on Deep Q Learning DQN)
## Include NIPS 2013 version and Nature Version DQN
I rewrite the code from another repo and make it much simpler and easier to understand Deep Q Network Algorithm from DeepMind
The code of DQN is only 160 lines long.
To run the code, just type python FlappyBirdDQN.py
Since the DQN code is a unique class, you can use it to play other games.
## About the code
As a reinforcement learning problem, we knows we need to obtain observations and output actions, and the 'brain' do the processing work.
Therefore, you can easily understand the BrainDQN.py code. There are three interfaces:
1. getInitState() for initialization
2. getAction()
3. setPerception(nextObservation,action,reward,terminal)
the game interface just need to be able to feed the action to the game and output observation,reward,terminal
## Disclaimer
This work is based on the repo: [yenchenlin1994/DeepLearningFlappyBird](https://github.com/yenchenlin1994/DeepLearningFlappyBird.git)
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DRL-FlappyBird-master.zip (38个子文件)
DRL-FlappyBird-master
saved_networks
network-dqn-50000.meta 72KB
network-dqn-50000 13.72MB
checkpoint 283B
FlappyBirdDQN.py 1KB
assets
sprites
4.png 3KB
redbird-midflap.png 3KB
3.png 3KB
base.png 664B
7.png 3KB
5.png 3KB
1.png 3KB
2.png 3KB
background-black.png 4KB
6.png 3KB
8.png 3KB
redbird-downflap.png 3KB
0.png 3KB
9.png 3KB
pipe-green.png 5KB
redbird-upflap.png 3KB
audio
swoosh.ogg 13KB
point.ogg 13KB
hit.ogg 15KB
wing.ogg 8KB
wing.wav 29KB
point.wav 173KB
die.ogg 17KB
die.wav 190KB
hit.wav 94KB
swoosh.wav 346KB
README.md 1KB
game
wrapped_flappy_bird.py 8KB
flappy_bird_utils.py 3KB
__pycache__
wrapped_flappy_bird.cpython-36.pyc 5KB
flappy_bird_utils.cpython-36.pyc 2KB
__pycache__
BrainDQN_Nature.cpython-36.pyc 6KB
BrainDQN_Nature.py 7KB
BrainDQN_NIPS.py 5KB
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