IBM Data Science Practice Test 2025 – Comprehensive Exam Prep

The development of AlphaGo involved sophisticated reinforcement learning techniques, which were crucial to its success in defeating the reigning European Champion. Reinforcement learning is a type of machine learning where an agent learns to make decisions by taking actions in an environment to maximize a cumulative reward.

In the case of AlphaGo, the system learned to play the game of Go through trial and error, adjusting its strategy based on the outcomes of its moves. Initially, it used supervised learning to mimic human experts by analyzing a dataset of professional games. However, the real power of AlphaGo emerged from its reinforcement learning phase, where it played countless games against itself. This self-play allowed it to discover new strategies and improve its gameplay dynamically, independent of any human guidance. The combination of reinforcement learning with deep neural networks enabled AlphaGo to develop a highly sophisticated understanding of the game, ultimately leading to its victory over top human players.

The other types of learning mentioned do not capture the methodology utilized by AlphaGo. Unsupervised learning focuses on finding patterns in data without labeled responses, while semi-supervised and supervised learning rely on labeled datasets to guide learning processes, which were supplementary to the reinforcement-focused approach that underpinned AlphaGo's path to success in competitive play.