A Java implementation of Tetris with a genetic AI.
The AI learns over time through a series of generations. Each member of a generation's population plays a game of Tetris with a set of scoring weights. For each piece playable in the game, the board is re-scored considering the all valid placements and rotations of the piece. The highest score is taken and the game continues. The score of the board is determined by summing each of the following properties multiplied by its associated weight.
| Name | Description |
|---|---|
| Sum Height | The sum of each column's height. |
| Max Height | The height of the tallest column. |
| Height Delta | The difference between teh tallest and shortest column. |
| Height Average | The average of each column's height. |
| Holes | The number of empty spaces below the top of a column. |
| Wells | The height delta for columns which are two or more blocks lower than both of its neighbors. |
| Blockades | The number of blocks placed directly over a hole. |
| Clears | How many clears occur from placing the block (up to four). |
The first generation begins with random weights. After all of the games for a generation have completed, the weights for the next generation's population is determined. The elite population (the 25% highest scoring members) of the previous generation are moved as-is into the next generation. The remaining candidates are constructed by randomly pairing two candidates from the upper 50% of the previous population and choosing each weight randomly from one or the other parent. With a 10% chance, the offspring will have a random mutation and get a completely new weight.
This process continues indefinitely.
Copyright (c) 2012 Eric Fritz
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