#PUNG

##Neural network controlled Pong clone

I was asked if it was possible to train a neural network to play a game in a similar style to how a human would play; this is my attempt to answer that question.

It works by collecting data from a human player, training a neural network on that data, and importing it back into the game.

##Compilation

You'll need SFML-dev, Box2D, and the UBLAS stuff from Boost. If you have that you should be able to just run make.

If you're going to try to compile this on Windows: best of luck to you, I haven't even tried.

On Debian install r-base and littler to get the neural network stuff working.

##How does it work?

If you just want to see it play with an already trained network, run:

pung -p 1 -f goalkeeper-100.csv -n 100

You should see the paddle stay in the middle and "dive" for the ball when it goes to his side of the court, hence goalkeeper.

If you want to train the network with your own data - first, you will need data. Run:

pung -p 0 -f <filename>

to start PUNG in data-collection mode and collect ~3MB of data, which should take around half an hour. Only give it the name of the file; it writes to data/human/<filename>. It appends to the file so you don't have to collect all of the data in one run; you can stop and start it as you wish with the same file name.

Secondly, you'll need R to train the neural network. The script is in src/learning/:

nnet.R [-i <in_file> -o <out_file> -t <training_set_size> -h <hidden_nodes> -a <learning rate> -e <number_of_epochs>]

The training set size is any number up to the number of lines in the <in_file>. You should experiment with the other values but here's the starting point:

• 50 hidden nodes
• 0.2 learning rate
• 1000 epochs

Once training is done, look at the image saved in img/ and confirm that the error with respect to epochs trends downwards. The result of training is saved to data/ai/<out_file>

Thirdly, run PUNG again with mode 1, that filename in data/ai/, and the number of hidden nodes used for training:

pung -p 1 -f <filename> -n 100

If everything went well, you should see the red paddle play in a similar style to how you played when recording that data.

The MIT License (MIT)

Copyright (c) 2015 Craig Lomax

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

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