AlphaZero

AlphaZero is a program developed by DeepMind, a subsidiary of Google, that achieved superhuman performance in the games of chess, shogi, and Go. It was announced in December 2018. Unlike previous chess engines that relied on extensive hand-crafted evaluation functions, AlphaZero learned to play these games through self-play, starting with no knowledge beyond the rules of the game.

The core of AlphaZero's success lies in its use of deep neural networks and Monte Carlo tree search (MCTS). The neural network takes the current board state as input and outputs two values: a policy, which indicates the probability of selecting each possible move, and a value, which estimates the likelihood of winning from that position. MCTS then uses these outputs to explore the game tree, prioritizing moves that the neural network deems promising and evaluating positions it encounters.

During its training, AlphaZero played millions of games against itself. Through this iterative process of playing, evaluating, and updating its neural network based on the outcomes, it progressively improved its playing strength. The program demonstrated remarkable strategic understanding and a novel style of play, often surprising human experts. Its victory over Stockfish, the reigning world champion chess engine at the time, was a significant milestone, showcasing the power of reinforcement learning for complex strategic games. AlphaZero's architecture and learning method have had a profound impact on artificial intelligence research, particularly in the fields of game playing and reinforcement learning.