Connect 4 michael yura.

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Presentation transcript:

connect 4 michael yura

what's this all about? create connect 4 create a dynamic ai for connect 4 use reinforcement learning training against other ai's test different “aggressiveness” of learning

ai outline keeps track of the moves it does for each board it is presented with examines if it has won or lost the game changes the way it places pieces in the future depending on the game's outcome (reinforcement learning) if won, will do what it did more often if lost, will do what it did less often

table data [0,0;1][0,1;0][0,2;0][0,3;0][0,4;0][0,5;0][1,0;0][1,1;0][1,2;0][1,3;0][1,4;0][1,5;0][2,0;0][2,1;0][2,2;0][2,3;0][2,4;0][2,5;0][3,0;0][3,1;0][3,2;0][3,3;0][3,4;0][3,5;0][4,0;0][4,1;0][4,2;0][4,3;0][4,4;0][4,5;0][5,0;0][5,1;0][5,2;0][5,3;0][5,4;0][5,5;0][6,0;0][6,1;0][6,2;0][6,3;0][6,4;0][6,5;0] [10.0,10.0,10.0,10.0,10.0,10.0,10.0,] [0,0;1][0,1;1][0,2;0][0,3;0][0,4;0][0,5;0][1,0;0][1,1;0][1,2;0][1,3;0][1,4;0][1,5;0][2,0;0][2,1;0][2,2;0][2,3;0][2,4;0][2,5;0][3,0;2][3,1;0][3,2;0][3,3;0][3,4;0][3,5;0][4,0;0][4,1;0][4,2;0][4,3;0][4,4;0][4,5;0][5,0;0][5,1;0][5,2;0][5,3;0][5,4;0][5,5;0][6,0;0][6,1;0][6,2;0][6,3;0][6,4;0][6,5;0] [10.0,10.0,10.0,10.0,10.0,10.0,10.0,] [0,0;0][0,1;0][0,2;0][0,3;0][0,4;0][0,5;0][1,0;0][1,1;0][1,2;0][1,3;0][1,4;0][1,5;0][2,0;0][2,1;0][2,2;0][2,3;0][2,4;0][2,5;0][3,0;0][3,1;0][3,2;0][3,3;0][3,4;0][3,5;0][4,0;0][4,1;0][4,2;0][4,3;0][4,4;0][4,5;0][5,0;1][5,1;0][5,2;0][5,3;0][5,4;0][5,5;0][6,0;0][6,1;0][6,2;0][6,3;0][6,4;0][6,5;0] [10.0,10.0,10.0,10.0,10.0,10.0,10.0,] [0,0;0][0,1;0][0,2;0][0,3;0][0,4;0][0,5;0][1,0;0][1,1;0][1,2;0][1,3;0][1,4;0][1,5;0][2,0;0][2,1;0][2,2;0][2,3;0][2,4;0][2,5;0][3,0;1][3,1;0][3,2;0][3,3;0][3,4;0][3,5;0][4,0;0][4,1;0][4,2;0][4,3;0][4,4;0][4,5;0][5,0;1][5,1;2][5,2;0][5,3;0][5,4;0][5,5;0][6,0;0][6,1;0][6,2;0][6,3;0][6,4;0][6,5;0] [10.0,10.0,10.0,10.0,10.0,10.0,10.0,] [0,0;1][0,1;1][0,2;1][0,3;0][0,4;0][0,5;0][1,0;0][1,1;0][1,2;0][1,3;0][1,4;0][1,5;0][2,0;2][2,1;0][2,2;0][2,3;0][2,4;0][2,5;0][3,0;2][3,1;0][3,2;0][3,3;0][3,4;0][3,5;0][4,0;0][4,1;0][4,2;0][4,3;0][4,4;0][4,5;0][5,0;0][5,1;0][5,2;0][5,3;0][5,4;0][5,5;0][6,0;0][6,1;0][6,2;0][6,3;0][6,4;0][6,5;0] [10.0,10.0,10.0,10.0,10.0,10.0,10.0,] [0,0;0][0,1;0][0,2;0][0,3;0][0,4;0][0,5;0][1,0;0][1,1;0][1,2;0][1,3;0][1,4;0][1,5;0][2,0;0][2,1;0][2,2;0][2,3;0][2,4;0][2,5;0][3,0;1][3,1;0][3,2;0][3,3;0][3,4;0][3,5;0][4,0;0][4,1;0][4,2;0][4,3;0][4,4;0][4,5;0][5,0;1][5,1;2][5,2;2][5,3;0][5,4;0][5,5;0][6,0;1][6,1;0][6,2;0][6,3;0][6,4;0][6,5;0] [10.0,10.0,10.0,10.0,10.0,10.0,10.0,]

table data [0,0;1][0,1;1][0,2;0][0,3;0][0,4;0][0,5;0][1,0;0][1,1;0][1,2;0][1,3;0][1,4;0][1,5;0][2,0;0][2,1;0][2,2;0][2,3;0][2,4;0][2,5;0][3,0;2][3,1;0][3,2;0][3,3;0][3,4;0][3,5;0][4,0;0][4,1;0][4,2;0][4,3;0][4,4;0][4,5;0][5,0;0][5,1;0][5,2;0][5,3;0][5,4;0][5,5;0][6,0;0][6,1;0][6,2;0][6,3;0][6,4;0][6,5;0] [10.0,10.0,10.0,10.0,10.0,10.0,10.0,]

table data [0,0;1][0,1;1][0,2;0][0,3;0][0,4;0][0,5;0] [1,0;0][1,1;0][1,2;0][1,3;0][1,4;0][1,5;0] [2,0;0][2,1;0][2,2;0][2,3;0][2,4;0][2,5;0] [3,0;2][3,1;0][3,2;0][3,3;0][3,4;0][3,5;0] [4,0;0][4,1;0][4,2;0][4,3;0][4,4;0][4,5;0] [5,0;0][5,1;0][5,2;0][5,3;0][5,4;0][5,5;0] [6,0;0][6,1;0][6,2;0][6,3;0][6,4;0][6,5;0] [10.0,10.0,10.0,10.0,10.0,10.0,10.0,]

looking ahead to 2nd quarter program ai to change its %'s depending on whether it wins or loses does an ai that is more “aggressive” (changes its %'s to a greater degree each time) learn to play better? Can an ai train itself?