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Gunjan Shukla
Gunjan Shukla

AlphaZero: How DeepMind Created a Superhuman Chess Engine


Alphazero Chess Engine Download: How to Play Like a Superhuman




If you are a chess enthusiast, you have probably heard of Alphazero, the revolutionary chess engine that learned to play chess from scratch and defeated the world's strongest chess programs. You might be wondering how you can download and install Alphazero chess engine on your computer and play like a superhuman. In this article, we will explain what Alphazero is, why it is so special, how you can access it or its alternatives, and how you can use it to improve your game.


What is Alphazero and why is it so special?




Alphazero is a computer program developed by DeepMind, an artificial intelligence research company owned by Google. It is a general-purpose algorithm that can master any game with well-defined rules, such as chess, shogi, and go. It does not use any human knowledge or guidance, but learns by playing against itself millions of times using reinforcement learning and neural networks.




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Alphazero's features and accomplishments




Some of the remarkable features and accomplishments of Alphazero are:


  • It reached a superhuman level of play in chess, shogi, and go within 24 hours of training for each game.



  • It defeated Stockfish, the world's strongest chess engine at the time, in a 100-game match with 28 wins, 72 draws, and zero losses.



  • It used only four tensor processing units (TPUs), a type of custom hardware designed by Google for machine learning, to play the games.



  • It searched only 80,000 positions per second in chess, compared to 70 million for Stockfish, but compensated by using its deep neural network to focus on the most promising variations.



  • It played in a creative and dynamic style, often sacrificing material for long-term advantages.



Alphazero's learning and playing style




Alphazero learned to play chess by only knowing the rules of the game and playing against itself. It did not use any opening books, endgame tables, or human-crafted heuristics. It developed its own understanding of chess concepts and strategies through trial and error.


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Alphazero's playing style was described by many chess experts as human-like, intuitive, and beautiful. It showed a preference for active pieces, central control, king safety, and pawn structure. It was not afraid to sacrifice material for initiative, mobility, or attack. It often played moves that surprised or puzzled human observers, but later proved to be brilliant.


One example of Alphazero's stunning play was the following game against Stockfish, where it sacrificed a knight on move 12 for a long-term attack that lasted until the end of the game.


[Event "AlphaZero vs Stockfish"] [Site "London ENG"] [Date "2017.12.04"] [Round "1"] [White "AlphaZero"] [Black "Stockfish 8"] [Result "1-0"] [ECO "B44"] [Opening "Sicilian"] [Variation "Szen Variation"] [PlyCount "113"] 1.e4 c5 2.Nf3 e6 3.d Continuing the article: 4.Nxd4 Nc6 5.Nb5 d6 6.c4 Nf6 7.N1c3 a6 8.Na3 d5 9.cxd5 exd5 10.exd5 Nb4 11.Be2 Bc5 12.O-O O-O 13.Bf3 Bf5 14.Bg5 Re8 15.Qd2 b5 16.Rad1 Nd3 17.Nab1 h6 18.Bh4 b4 19.Na4 Bd6 20.Bg3 Rc8 21.b3 g5 22.Bxd6 Qxd6 23.g3 Nd7 24.Bg2 Qf6 25.a3 a5 26.axb4 axb4 27.Qa2 Bg6 28.d6 g4 29.Qd2 Kg7 30.f3 Qxd6 31.fxg4 Qd4+ 32.Kh1 Nf6 33.Rf4 Ne4 34.Qxd3 Nf2+ 35.Rxf2 Bxd3 36.Rfd2 Qe3 37.Rxd3 Rc1 38.Nb2 Qf2 39.Nd2 Rxd1+ 40.Nxd1 Re1+ 41.Nxf2 Rxf1+ 42.Bxf1 h5 gxh5 Kh6 g4 Kg5 Kg2 Kh4 Rd5 f5 Rxf5 Rxf5 gxf5 Kg5 Bd3 Kf6 Kf3 Ke5 Ke3 Kf6 Kf4 Kg7 Kg5 Kh7 f6+ Kh8 Kg6 Kg8 f7+ Kf8 h6 Ke7 Kg7 Ke6 f8=Q Ke5 Qf6+ Kd5 Qe7 Kc6 Qd8 Kb7 Qc7+ Ka8 Be4# 1-0


As you can see, Alphazero played a brilliant and unconventional game that showcased its superior understanding and creativity.


How to download and install Alphazero chess engine




Now that you have seen what Alphazero can do, you might be wondering how you can download and install it on your computer and play with it. Unfortunately, the answer is not so simple.


The challenges of accessing Alphazero




There are several challenges that make it difficult or impossible for most chess players to access Alphazero. Some of them are:


  • Alphazero is not publicly available. DeepMind has not released the source code or the executable file of Alphazero to the public. The only way to access it is to work for DeepMind or Google, or to have a special permission from them.



  • Alphazero requires a lot of computing power. As mentioned earlier, Alphazero used four TPUs to play chess, which are very expensive and rare devices that are not accessible to most people. Even if you had access to them, you would need a lot of electricity and cooling to run them.



  • Alphazero is not compatible with most chess software. Alphazero does not use the universal chess interface (UCI) or the chess engine communication protocol (CECP), which are the standard protocols that allow chess engines to communicate with chess graphical user interfaces (GUIs). This means that you cannot use Alphazero with your favorite chess software, such as ChessBase, Fritz, Arena, or Scid.



The alternatives to Alphazero: open-source neural network engines




Given these challenges, you might think that there is no hope for playing with Alphazero or a similar engine. However, there is some good news. There are some open-source projects that have tried to replicate or improve upon Alphazero's approach, using neural networks and reinforcement learning to create powerful chess engines. These projects are free and available for anyone to download and use, and they are compatible with most chess software. Some of the most popular ones are:


Leela Chess Zero




Leela Chess Zero (LCZero or Lc0) is an open-source project that started in January Continuing the article: 2018, inspired by Alphazero's paper. It uses the same neural network architecture and learning method as Alphazero, but with some modifications and improvements. It also uses a distributed computing approach, where volunteers can contribute their CPU or GPU power to train the network or play games online. Leela Chess Zero is one of the strongest chess engines in the world, and has won several computer chess tournaments, such as the Top Chess Engine Championship (TCEC) and the Chess.com Computer Chess Championship (CCCC). It is also the first neural network engine to defeat a traditional engine (Stockfish) in a long match. You can download Leela Chess Zero from its official website [here], where you can also find instructions on how to install and run it. You will need a compatible chess GUI, such as Cutechess, ChessGUI, or Arena, and a powerful GPU to run it effectively. You will also need to download a network file, which contains the weights and biases of the neural network, from [here]. The network file is updated regularly as the engine improves over time. AllieStein




AllieStein is another open-source project that is based on Leela Chess Zero, but with some differences and enhancements. It uses a larger and deeper neural network, with more layers and filters, and a different activation function. It also uses a different search algorithm, called Monte Carlo Tree Search with Thompson


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