ELF OpenGo: An Analysis and Open Reimplementation of AlphaZero the properties of AlphaZero-style algorithms. Typically humans play with computer for months before they allow proper "match". In AlphaGo’s case this involved analyzing millions of moves made by human go experts and playing many, many games against itself to reinforce what it learned. For the uninitiated, Stockfish 8 won the 2016 top chess engine championship and is probably the strongest chess engine right now. Also AlphaZero didn't run on a supercomputer in the match. AlphaGo … Click on the image for a larger version. Some of the games were released which have led to a bunch of interesting analysis. For humans, chess may take a lifetime to master. for every single run computer should not rely on some "random" variables. AlphaZero replaces hand-crafted heuristics with a deep neural network and algorithms that are given nothing beyond the basic rules of the game. I.e. In each opening, AlphaZero defeated Stockfish. AlphaZero, a game-playing algorithm designed by Google’s DeepMind project, is different. The paper claims that it looks at "only" 80,000 positions per second, compared to Stockfish's 70 million per second. Let’s start with Stockfish 8. Over the course of millions of AlphaGo vs AlphaGo games, the system progressively learned the game of Go from scratch, accumulating thousands of years of human knowledge during a period of just a few days. It is a real human vs machine battle. As an FM I'd get much more enjoyment of playing AlphaZero vs a regular engine. … a program referred to as DQN, which achieved human-beating proficiency in Atari video games using only pixels and game scores as input; AlphaGo, the … Only the training was done on what qualifies as a supercomputer (5000 first-generation TPUs to generate games, 64 second-generation TPUs to train the neural networks; 4 second-generation TPUs to play against Stockfish). More significant, perhaps, is that AlphaZero didn't just beat Stockfish. StockFull won the match with a score of +2, =7, -1. I am sure that will influence the top players. In the left bar, AlphaZero plays White; in the right bar, AlphaZero is Black. AlphaZero already seems to play like a regular "centaur" -> correspodence GM with an engine assistance. Source: Reuters . An awful lot of ideas can also be discovered by trying to think like AlphaZero, trying to maybe challenge for yourself the conclusions of engines and finding gaps in there. AlphaZero’s victory is just the latest in a series of computer triumphs over human players since Computer programs have been able to beat the best IBM’s Deep Blue defeated Garry Kasparov in … It should be pointed out that AlphaZero had effectively built its own opening book, so a fairer run would be against a top engine using a good opening book. Computer must do deterministic and recoverable moves. Ultimately, our mission was to find an adjustment to the rules to allow more space for human creativity. To verify the robustness of AlphaZero, we also played a series of matches that started from common human openings. Here's some YouTube videos with quality analysis of the games from this match: Interesting positional game; Zugzwang Game; Evans Gambit game; One of the most interesting things about these … AlphaZero searches just 80 thousand positions per second in chess, compared to 70 million for Stockfish. And AlphaZero can do more than just play chess. In late 2017, we introduced AlphaZero, a single system that taught itself from scratch how to master the games of chess, shogi, and Go, beating a world-champion computer program in each case. It beat Stockfish's programmers. One comparison would be it plays like Karpov would with perfect tactics.