Deepmind presents Artificial General Intelligence for board games

In a paper recently published in the journal Science, researchers from DeepMind describe Alpha Zero, a system that mastered three very complex games, Go, chess, and shogi, using only self-play and reinforcement learning. What is different in this system (a preliminary version was previously referred in this blog), when compared with previous ones, like AlphaGo Zero, is that the same learning architecture and hyperparameters were used to learn different games, without any specific customization for each different game.
Historically, the best programs for each game were heavily customized to use and exploit specific characteristics of that game. AlphaGo Zero, the most impressive previous result, used the spatial symmetries of Go and a number of other specific optimizations. Special purpose chess program like Stockfish took years to develop, use enormous amounts of field-specific knowledge and can, therefore, only play one specific game.
Alpha Zero is the closest thing to a general purpose board game player ever designed. Alpha Zero uses a deep neural network to estimate move probabilities and position values. It performs the search using a Monte Carlo tree search algorithm, which is general-purpose and not specifically tuned to any particular game. Overall, Alpha Zero gets as close as ever to the dream of artificial general intelligence, in this particular domain. As the authors say, in the conclusions, “These results bring us a step closer to fulfilling a longstanding ambition of Artificial Intelligence: a general game-playing system that can master any game.
While mastering these ancient games, AlphaZero also teaches us a few things we didn’t know about the games. For instance, that, in chess, white has a strong upper hand when playing the Ruy Lopez opening, or when playing against the French and Caro-Kann defenses. Sicilian defense, on the other hand, gives black much better chances. At least, that is what the function learned by the deep neural network obtains…
Actualization: The NY Times just published an interesting piece on this topic, with some additional information.