“In this paper we have introduced a method that combines the benefits of both approaches.
“Many of the breakthroughs in artificial intelligence have been based on either high-performance planning,” wrote the researchers. There are very concrete lessons that can be applied in artificial intelligence in a very practical setting. Of course, there is much more to this than just chess, Go, or PacMan. Similar performances were reported in the Atari games, where MuZero outperformed state-of-the-art engines in 42 out of 57 games. MuZero slightly exceeded the performance of AlphaZero despite using less overall computation, which seems to indicate that MuZero has a deeper understanding of the game and the positions it was playing. The most impressive results came from Go, a game that is unfathomably more complex than chess. Researchers didn’t only train the engine in chess, they also trained it in go, shogi, and 57 Atari games commonly used in this sort of study. Although the details that researchers presented are sparse, they claim that MuZero achieved the same performance as AlphaZero. This led to remarkably good performances. The Elo evaluation of MuZero throughout training in chess, shogi, Go, and Atari. Researchers say that this also allows MuZero to develop an internal intuition regarding the rules of the game. Now, Google’s researchers have taken things to the next level with MuZero. It’s a surprisingly human way to approach the game, although many of AlphaZero’s moves seem distinctly inhuman. Instead of playing moves that it can fully calculate to be better, which is what most algorithms do, AlphaZero plays moves that seem better. For instance, AlphaZero would often sacrifice a piece with no immediate reward in sight - it itself doesn’t necessarily calculate all the outcomes.
Unlike traditional algorithms which play very concrete, grinding type of games, AlphaZero tends to play in a very conceptual and creative way (though the word ‘creative’ will surely annoy some readers). Intriguingly, this not only enabled it to achieve remarkable prowess, but also to develop a style of its own. Instead, it was only taught the rules of the game and allowed to play against itself over and over. But Google’s AlphaZero, for instance, takes a very different approach.AlphaZero has become, arguably, the best chess-playing entity in the world without studying a single human game.
#Stockfish chess make ai faster how to
Traditionally, chess algorithms were trained in a very straightforward way: they were taught the rules of the game, fed a huge database of games, taught how to calculate, and off they went.
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