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Home » AlphaGo Zero: An Unveiling of the Pioneering Algorithm

AlphaGo Zero: An Unveiling of the Pioneering Algorithm

AlphaGo Zero, an initiative of DeepMind, part of the Google conglomerate, marks a revolutionary milestone in the field of Artificial Intelligence (AI) and game theory. Unlike its predecessor, AlphaGo, which learnt to play the game of Go by studying a vast number of professional human games, AlphaGo Zero developed its Go playing abilities starting from scratch – learning entirely by playing against itself without any prior human knowledge.

The Algorithm

AlphaGo Zero utilizes a unique combination of machine learning techniques, incorporating deep neural networks with a Monte Carlo Tree Search (MCTS) to guide its decision-making processes. It does not rely on previous game records or human intervention but evolves its strategy by playing millions of games against itself, a concept known as reinforcement learning.

A single neural network, known as the “policy and value” network, is used in AlphaGo Zero. It outputs both move probabilities and a value, estimating the probability of winning in the current position. This dual utilization contrasts with the original AlphaGo, which used two separate networks: a policy network to suggest moves and a value network to predict the game’s outcome.

The core algorithm of AlphaGo Zero, at its essence, comprises two major stages: self-play data generation and network training.

Self-Play Data Generation: AlphaGo Zero begins with random plays, where two neural networks with the same parameters play against each other. The game starts with an empty board, and the players take turns, with the neural network making predictions and selecting moves. The MCTS is used to simulate multiple possible outcomes and ultimately choose the most promising move. Each game’s outcome provides a new learning example, thereby refining the neural network’s understanding of effective strategies.

Network Training: The policy and value network are then trained using the latest self-play data. The policy network training aims to increase the probability of the played move, while the value network training tries to reduce the error between the predicted and actual game outcomes. Once trained, this updated network participates in further self-play games, which yield more self-play data, driving a reinforcement learning cycle that leads to continual performance improvement.

Results and Impact

AlphaGo Zero demonstrated astonishing performance by defeating the previous champion version of AlphaGo by 100 games to 0 after just 36 hours of self-play. Over time, AlphaGo Zero independently rediscovered many of the strategies found in thousands of years of human Go playing history and eventually surpassed them, inventing new strategies and creative moves that overturned centuries of received wisdom.

Conclusion

AlphaGo Zero represents a significant leap forward in the realm of AI research, demonstrating that machines can learn from a blank state, devoid of human intervention. Its success challenges our understanding of learning systems and has vast implications for a variety of real-world applications, from protein folding to reducing energy consumption. While Go provides a suitable environment to explore these AI techniques, the principles behind AlphaGo Zero are broadly applicable to many other tasks and fields. Its innovative and groundbreaking approach sets a precedent for future AI systems and reinforces the immense potential of AI as a tool for problem-solving and discovery.