In the context of AlphaGo, the systematic removal of components from the neural network architecture to assess their individual contributions is crucial. This process, often involving disabling specific layers, features, or algorithmic elements, allows researchers to understand the importance of each part for the overall performance of the system. For instance, removing the policy network and observing the change in playing strength would quantify its significance.
Understanding the effect of individual architectural elements provides several benefits. It allows for the identification of redundant or less important components, leading to model simplification and improved efficiency. Furthermore, this methodology offers valuable insights into the learned representations and decision-making processes of the AI, fostering a deeper comprehension of its capabilities and limitations. Historically, these techniques have been instrumental in refining neural network architectures across various domains, not just in game-playing AIs.
