How did they solve the state representation problem?

The state representation problem was addressed primarily through advancements in machine learning and reinforcement learning techniques. Researchers developed more sophisticated algorithms that allowed for effective encoding of states in complex environments, utilizing deep learning to extract relevant features from high-dimensional data. Techniques like state abstraction and hierarchical reinforcement learning also emerged, enabling agents to represent and navigate large state spaces more efficiently. This combination of methods improved decision-making and problem-solving capabilities in various applications.