AlphaZero defeats the best engines in chess, shogi and Go

Not so long ago AlphaGo Zero celebrated the triumphs in the game Go, and now DeepMind presented a new version called AlphaZero, which is more generic, because it faced the strongest engines in games such as:

• chess: the Stockfish 8 engine
• shogi (Japanese chess variant): Elmo engine
• and again Go: AlphaGo Zero (ie earlier version, learned 3 days)

AlphaZero knew only the rules of a given game and learned based on the analysis of games with oneself. Overcoming the existing engines took place:

• after 4h for chess (300,000 iterations)
• after 2 hours for shogi (100,000 iterations)
• after 8h for Go (165,000 iterations)

After the learning process, the tournament was played. The results are: (W - wins, D - draws, P - loses):

As we can see in the game of chess AlphaZero did not record a single defeat (28 wins and 72 draws). In shogi it was 90 wins, 2 draws and 8 defeats. The most even situation was in the case of the game in Go, which ended with the result of 60 wins and 40 defeats.

The AlphaZero algorithm is based on a deep neural network. It is running on 4 TPU. The algorithm settings, network structure and hyper-parameters were the same for each game.

There were comments from chess players that AlphaZero plays like a madman, making moves that would not come to anyone's head in a given position. However, as it turned out later, they always had their justification.

To sum up:

• the program used a neural network with the same structure and the same parameters for each game, it is another step towards creating a universal algorithm
• it took only a few hours of learning from self-play to surpass the strongest chess engines (which were developed for years)
• hardware requirements limited to 4 TPUs
• extension to further application areas is only a matter of time

Source: https://arxiv.org/pdf/1712.01815.pdf