AlphaGo garnered global attention when it won a 2016 match against South Korean Go world champion Lee Sedol, the first computer program to beat a world champion at the game of Go. The successful application of AlphaGo has opened up a broad future for the development of artificial intelligence technology.
Principle of technical realization
The implementation of AlphaGo is based on deep learning and reinforcement learning technology. It conducts deep learning by learning human players' chess records and self-play, and uses reinforcement learning for training and optimization. AlphaGo uses a deep neural network and a Monte Carlo tree search algorithm to maximize the predicted winning rate, so that it can perform well in Go games.
Specifically, the implementation process of AlphaGo includes the following steps:
- Learning: Learn Go knowledge from a large number of human chess records and self-play, and build a deep neural network model. In the learning phase, AlphaGo uses a deep neural network (DNN) to learn how human players play chess and strategies, thereby obtaining high-level Go knowledge.
- Prediction: Use the Monte Carlo tree search algorithm for prediction and optimization to improve the prediction accuracy and search efficiency of the model. In the prediction stage, AlphaGo uses the trained deep neural network to predict the position and winning rate of the next move in Go. Here we need to use the Monte Carlo Tree Search (MCTS) algorithm for prediction and optimization. MCTS is a search algorithm based on Monte Carlo simulation, which can find the optimal solution in a large-scale search space.
- Optimization: Model training and optimization through reinforcement learning to improve the overall level and performance of the model. In the optimization phase, AlphaGo uses reinforcement learning algorithms to continuously optimize its performance. Reinforcement learning is a trial-and-error based learning method, which optimizes the performance of the model through continuous trial and error and feedback. During the optimization process of AlphaGo, it will play itself, constantly trying to improve its winning rate by continuously optimizing its own strategy.
The follow-up progress of AlphaGo
AlphaGo Zero
In 2017, DeepMind launched a new version of AlphaGo, AlphaGo Zero. Unlike the original AlphaGo, AlphaGo Zero does not use human chess records, but learns through self-play, building its own Go knowledge from scratch. AlphaGo Zero realized self-play and reinforcement learning through Monte Carlo tree search and deep neural network, and finally achieved a higher winning rate and better performance.
AlphaZero
AlphaZero is a new generation of artificial intelligence computer program based on AlphaGo Zero by DeepMind. Similar to AlphaGo Zero, AlphaZero learns by playing against itself, but it's not limited to Go, but also covers a variety of board games such as chess and Japanese shogi. AlphaZero achieves self-play and reinforcement learning through deep neural network and Monte Carlo tree search, and has achieved achievements beyond human level in various board games.