Curriculum Learning Solves 2048 and Tetris

Artificial intelligence research has achieved a significant milestone by successfully mastering complex games like 2048 and Tetris through a technique known as curriculum learning. This approach mimics human education, where learning is structured from simple to complex concepts. By training AI agents on progressively harder versions of a game, researchers have observed a dramatic improvement in the agents' ability to solve the full task.

The core principle involves breaking down a difficult problem into manageable sub-tasks. For instance, an AI learning to play 2048 might first be trained on a smaller grid or with fewer possible moves. As its performance improves, the difficulty is incrementally increased. This prevents the agent from becoming overwhelmed and allows it to build a foundational understanding before tackling the ultimate challenge. The results indicate that this method is highly effective for problems where the reward is sparse or the state space is vast, offering a powerful new tool for developing more capable and efficient AI systems.

The concept of curriculum learning represents a paradigm shift in how machine learning models are trained. Traditional methods often expose the AI to the full complexity of a task from the very beginning, which can lead to inefficient learning or the agent getting stuck in suboptimal strategies. In contrast, curriculum learning provides a guided path to mastery.

Researchers applied this methodology to two distinct and challenging games: 2048, a puzzle game requiring long-term planning, and Tetris, a classic arcade game demanding rapid reflexes and spatial reasoning. The process typically involves several key stages:

• Defining a series of sub-tasks, ordered by difficulty.
• Training the agent on the simplest sub-task until it reaches a performance threshold.
• Gradually introducing more complex sub-tasks.
• Finally, testing the agent on the original, full-complexity game.

This structured approach allows the AI to develop robust strategies and generalize its knowledge, leading to superior performance compared to agents trained without a curriculum.

The application of curriculum learning to 2048 and Tetris yielded impressive results, demonstrating the method's versatility. For 2048, which involves merging numbered tiles on a 4x4 grid, the curriculum might start with a 3x3 grid and gradually expand to the standard 4x4. This helps the agent learn tile management and corner-building strategies effectively.

For Tetris, the curriculum could involve starting with a narrower playfield or slower game speeds. This allows the agent to first learn how to clear lines efficiently before having to manage the increased pace and complexity of the standard game. The final performance of the curriculum-trained agents was measured against their ability to achieve high scores and survive for extended periods. In both cases, the agents developed strategies that were not only effective but often mirrored or exceeded human-level play, showcasing the power of this learning framework.

The success of curriculum learning in gaming environments has significant implications for the broader field of artificial intelligence. Many real-world problems, from robotics to natural language processing, share characteristics with these games: large state spaces, delayed rewards, and complex decision-making processes. The principles of scaffolding and gradual difficulty increase can be directly applied to these domains.

For example, a robot learning to perform a complex manipulation task could first be trained on simpler movements, and an AI learning to write code could start with basic functions before tackling entire programs. This research provides strong evidence that structuring the learning process is a key ingredient for developing AI that can solve complex, multi-step problems. It moves us closer to creating more general and adaptable AI systems that can learn efficiently and effectively in a wide range of environments.