[off-list] Keith,
Very cool idea. I would love you to come to our next hackathon. I think it's going to be Nov 2-3 in San Francisco. I might be able to set you up with a free hotel room or travel stipend. Let me know if you think you might be able to make it. --------- Matt Taylor OS Community Flag-Bearer Numenta On Sun, Aug 25, 2013 at 7:31 PM, Matt Keith <[email protected]> wrote: > Since I live in Colorado and wasn't able to make it to the last hackathon, > I held my own last night and came up with this little example. It is the > classic text based skiing game where you have to move the skier left or > right to keep him on the ski slope without hitting the trees. > > I have been very intrigued by the idea that motor control could be just > another form of prediction. So I thought that I could train a model on a > perfect run of the game and then use the predictions that it generates to > move the skier in a live game. > > It starts by feeding the model 1000 lines of perfect skier positions in a > ramdomly generated ski slope. A slope line consists of 80 characters with > two trees as boundaries and the skier (hopefully) in the middle. > > | H | > Tree Skier Tree > > The slope line is encoded as three integer values to pass into the model: > the left tree position, the skier position, and the right tree position. > > Given the current ski slope line, we ask the model to predict the next > skier position. If the position is greater than the current value, the > skier is moved to the right one space. If the position is less than the > current value, the skier is moved to the left one space. Otherwise, the > skier position is left alone. > > You can download the code and play for yourself at: > https://github.com/keithcom/nta_ski > > Building and running this test app has raised a few more questions for me. > > 1. Is there a string encoder yet? Originally, I wanted to just send the > model the full ski slope line, but converted it to the int array to pass in > scaler values. > 2. The trained model seems to work for a little bit, but then stops as it > keeps learning during the live run. I would like the model to "see" the > results of it's prediction (i.e. how it moved the skier to complete the > feedback loop), but I also want to have some kind of error value so it > knows that it's prediction was not optimal. Does a mechanism like this > exist in the current code? > 3. Most of the model settings are just copied directly from the hotgym > example. Should I change some of the values to work better in this > scenario? > 4. Any other comments or suggestions to improve the demonstration? > > Thanks, > > Matt > > > _______________________________________________ > nupic mailing list > [email protected] > http://lists.numenta.org/mailman/listinfo/nupic_lists.numenta.org >
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