Deep Q-learning for playing tetris game
License: MIT License
Here is my python source code for training an agent to play Tetris. It could be seen as a very basic example of Reinforcement Learning's application.
Tetris demo
The demo could also be found at youtube demo
With my code, you can:
You could find my trained model at trained_models/tetris
Thank you very much for sharing! I am just wondering how many epochs did you train to reach the checkpoint that you posted.
hey there buddy, I get this error while trying to run test.py.
AttributeError: 'DeepQNetwork' object has no attribute 'fc1'
Checking your class, there's an fc1 attribute but you're using it as a function?
Why is the game so blurry?
Hey! Great work, that could be a great example in ML courses.
It would maybe benefit from having a pip requirement.txt files that would automatize the install:
Pillow==7.0.0
opencv-python==4.2.0.32
torch==1.4.0
numpy==1.18.2
matplotlib==3.0.3and the installation would sum up to:
python3 -m pip install -r requirement.txtWhat does env.get_next_states() return?
'next_state' shows
tensor([ 0., 75., 5., 107.], device='cuda:0')
'next_actions' shows
((0, 0),
(1, 0),
... ,
(8, 3))
Found that when running this on my machine the Tetris pieces were incredibly blurry.
Changing Line 242 in src/tetris.py from
img = img.resize((self.width * self.block_size, self.height * self.block_size))
to
img = img.resize((self.width * self.block_size, self.height * self.block_size),0)
solved the issue by forcing 0 antialiasing on resize.
Python 3.8
Mac OS 10.15.3
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