Solve Hi-Q with AlphaZero and Curriculum Learning (www.robw.fyi)

A tech enthusiast successfully applied deep learning, notably using the AlphaZero architecture, to solve the classic puzzle game Hi-Q after struggling with traditional programming approaches. Initially attempting constraint-based and reinforcement learning methods, he encountered challenges like large search spaces and local maxima in training. Ultimately, he found that incorporating curriculum learning — where the agent learned from easier game states before tackling the full challenge — significantly enhanced the model’s performance. This experiment highlights the importance of innovative training techniques in AI and underscores the limitations of traditional programming methods for solving complex problems. By leveraging AlphaZero's Monte Carlo Tree Search with enhanced planning capabilities, the author was able to achieve an optimal solution within six hours on modern hardware. While recognizing that this approach may not scale to more complex scenarios, the project emphasizes the value of accessible coding methodologies and the exploration of new paradigms in AI research.