Tetris AI with Genetic Algorithm

Idea: An AI agent learns to play the classic game Tetris using a Genetic Algorithm (GA) without pre-defined strategies.

Method:

Each individual (chromosome) represents weights for evaluating board states (lines cleared, holes, height, bumpiness).

GA evolves through selection, crossover, and mutation to improve performance over generations.

Tools: Python, NumPy, Pygame.

Outcome: The AI improves gradually, clearing more lines, reducing holes, and achieving higher scores.

Importance: Demonstrates how evolutionary algorithms can optimize game-playing strategies.