AlphaGo is an artificial intelligence program developed by DeepMind, designed to play the board game Go. Go is a highly complex game with many possible moves, which makes it challenging for traditional AI approaches. AlphaGo uses a combination of deep neural networks and reinforcement learning to learn from vast amounts of data and improve its gameplay strategy. By playing against itself and analyzing countless outcomes, AlphaGo was able to develop a nuanced understanding of the game far beyond basic strategies.
Reinforcement learning (RL) is a key component of how AlphaGo operates. In RL, an agent learns to make decisions by receiving feedback from its actions in the form of rewards or penalties. AlphaGo employs this method by playing millions of games against itself. Each time it plays, it updates its model based on the game's outcome. For example, if a certain move leads to a win, the model increases the value assigned to that move in similar situations. Conversely, if a move results in a loss, that move's value is decreased. This feedback loop allows AlphaGo to refine its strategy continuously, leading to improved performance over time.
Additionally, AlphaGo integrates supervised learning to analyze data from human expert games before engaging in self-play. This approach helps the model start with a strong foundation, using historical games to understand effective strategies. The combination of these methods enables AlphaGo not only to excel in the game but to also innovate new strategies that even seasoned players had not seen before. By leveraging reinforcement learning and deep learning techniques, AlphaGo illustrated how AI can achieve a high level of expertise in tasks previously thought to require human intuition and skill.