##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.
You'll need SFML-dev, Box2D, and the UBLAS stuff from Boost. If you have that
you should be able to just run
If you're going to try to compile this on Windows: best of luck to you, I haven't even tried.
On Debian install
littler to get the neural network stuff
##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
Secondly, you'll need R to train the neural network. The script is in
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
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.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.