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My project is inspired by one of my favorite past times: PC strategy games. I am ultimately attempting to research ways in which AIs can change and adapt their strategies based upon the play history of a particular opponent, hopefully a human opponent. To accomplish this, I am researching and implementing a subset of Machine Learning known as genetic algorithms.

The test game that I'm working with is, unfortunately, not a full fledged strategy game, but is nonetheless interesting. While I do no know the name of the game, the rules are as follows:

Species Layout

Current species layout is representing all game states (a combination of cards remaining in the stack, including the current flop, and the cards left in both my hand and my opponents) and mapping that to a table containing the flop card and what card to play. This way there are two dimensions of cross-breeding possible, along the X and Y axis. I'll post an example once I get more of this down.

Fitness Function

This is kind of the crux of the whole algorithm. The fitness function should in a perfect world be testing the policy representation of a player. However if I could do that, then I would already be done I suspect. So for now I'll be using a fairly non-descript and simple policy to test the algorithm on.


That is all for now

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