Reinforcement Learning for Tetris Game with Genetic Algorithms

Le résumé fourni par la source

Tetris is a classic tile-matching puzzle video game that Alexey Pajitnov designed and developed in 1984. Despite seeming simple, Tetris poses a significant challenge for Artificial Intelligence (AI) systems due to its dynamic gameplay and complex strategies. At its core, Tetris is recognized as an NP- complete problem, primarily due to the unpredictable sequence of falling tetrominoes and the vast, complex state space it generates. These features require quick and flexible decision- making in real-time. Traditional AI methods, based on heuristics and rules, often struggle. They depend on fixed evaluation functions and rigid strategies, which make them ineffective, especially during fast-paced, late-game situations where adaptability matters most. As a result, AI agents often face problems like poor convergence, weak generalization across different game states, and slow responses to unexpected game changes. This leads to inconsistent and less effective performance. To tackle these challenges, we need a strong and flexible Reinforcement Learning (RL) framework. Unlike static methods, RL enables agents to learn the best behaviors dynamically by interacting with their environment. They can adjust their strategies based on experience and improve their performance over time. This makes RL a promising approach for handling the complex and ever-changing nature of Tetris.

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