PongNet

4 layer ConvNet playing the Atari game Pong:

Added the saved JSON model architecture and the training weights in the files pong_model.json and pong_weights.h5.

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Prerequisites and packages

Run the code using python3 onwards:

numpy
tensorflow
keras
time
sys

Install the following packages:

sudo pip3 install --upgrade numpy tensorflow keras

Note: Install TensorFlow as per instructions on the TensorFlow website

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Running the pre-trained model

Copy and paste the following line in your command line in the PongNet directory to render the Pong game environment, load the model and play against the opponent:

python3 pong.py

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Training the model from scratch

Copy and paste the following line in your command line in the PongNet directory to start collecting the raw training data and training the model on the new data:

python3 pong.py 250000 200

where 250000 is the number of games and 200 is the number of time-steps per game. Training the model with these arguments takes about 2 days on a standard issue MacBook Pro CPU.