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AlphaZero is a computer program developed by artificial intelligence research company DeepMind to master the games of chess, shogi and go. This algorithm uses an approach similar to AlphaGo Zero.
On December 5, 2017, the DeepMind team released a preprint paper introducing AlphaZero, which would soon play three games by defeating world-champion chess engines Stockfish, Elmo, and the three-day version of AlphaGo Zero. In each case it made use of custom tensor processing units (TPUs) that the Google programs were optimized to use. AlphaZero was trained solely via self-play using 5,000 first-generation TPUs to generate the games and 64 second-generation TPUs to train the neural networks, all in parallel, with no access to opening books or endgame tables. After four hours of training, DeepMind estimated AlphaZero was playing chess at a higher Elo rating than Stockfish 8; after nine hours of training, the algorithm defeated Stockfish 8 in a time-controlled 100-game tournament (28 wins, 0 losses, and 72 draws). The trained algorithm played on a single machine with four TPUs.
DeepMind's paper on AlphaZero was published in the journal Science on 7 December 2018. While the actual AlphaZero program has not been released to the public, the algorithm described in the paper has been implemented in publicly available software. In 2019, DeepMind published a new paper detailing MuZero, a new algorithm able to generalize AlphaZero's work, playing both Atari and board games without knowledge of the rules or representations of the game.




Relation to AlphaGo Zero



AlphaZero (AZ) is a more generalized variant of the AlphaGo Zero (AGZ) algorithm, and is able to play shogi and chess as well as Go. Differences between AZ and AGZ include:

AZ has hard-coded rules for setting search hyperparameters.
The neural network is now updated continually.
AZ doesn't use symmetries, unlike AGZ.
Chess or Shogi can end in a draw unlike Go; therefore, AlphaZero takes into account the possibility of a drawn game.




Stockfish and Elmo



Comparing Monte Carlo tree search searches, AlphaZero searches just 80,000 positions per second in chess and 40,000 in shogi, compared to 70 million for Stockfish and 35 million for Elmo. AlphaZero compensates for the lower number of evaluations by using its deep neural network to focus much more selectively on the most promising variation.




Training


AlphaZero was trained by simply playing against itself multiple times, using 5,000 first-generation TPUs to generate the games and 64 second-generation TPUs to train the neural networks. Training took several days, totaling about 41 TPU-years. It cost 3e22 FLOPs.
In parallel, the in-training AlphaZero was periodically matched against its benchmark (Stockfish, Elmo, or AlphaGo Zero) in brief one-second-per-move games to determine how well the training was progressing. DeepMind judged that AlphaZero's performance exceeded the benchmark after around four hours of training for Stockfish, two hours for Elmo, and eight hours for AlphaGo Zero.




Preliminary results






= Outcome

=



Chess

In AlphaZero's chess match against Stockfish 8 (2016 TCEC world champion), each program was given one minute per move. AlphaZero was flying the English flag, while Stockfish the Norwegian. Stockfish was allocated 64 threads and a hash size of 1 GB, a setting that Stockfish's Tord Romstad later criticized as suboptimal. AlphaZero was trained on chess for a total of nine hours before the match. During the match, AlphaZero ran on a single machine with four application-specific TPUs. In 100 games from the normal starting position, AlphaZero won 25 games as White, won 3 as Black, and drew the remaining 72. In a series of twelve, 100-game matches (of unspecified time or resource constraints) against Stockfish starting from the 12 most popular human openings, AlphaZero won 290, drew 886 and lost 24.



Shogi

AlphaZero was trained on shogi for a total of two hours before the tournament. In 100 shogi games against Elmo (World Computer Shogi Championship 27 summer 2017 tournament version with YaneuraOu 4.73 search), AlphaZero won 90 times, lost 8 times and drew twice. As in the chess games, each program got one minute per move, and Elmo was given 64 threads and a hash size of 1 GB.



Go

After 34 hours of self-learning of Go and against AlphaGo Zero, AlphaZero won 60 games and lost 40.




= Analysis

=
DeepMind stated in its preprint, "The game of chess represented the pinnacle of AI research over several decades. State-of-the-art programs are based on powerful engines that search many millions of positions, leveraging handcrafted domain expertise and sophisticated domain adaptations. AlphaZero is a generic reinforcement learning algorithm – originally devised for the game of go – that achieved superior results within a few hours, searching a thousand times fewer positions, given no domain knowledge except the rules." DeepMind's Demis Hassabis, a chess player himself, called AlphaZero's play style "alien": It sometimes wins by offering counterintuitive sacrifices, like offering up a queen and bishop to exploit a positional advantage. "It's like chess from another dimension."
Given the difficulty in chess of forcing a win against a strong opponent, the +28 –0 =72 result is a significant margin of victory. However, some grandmasters, such as Hikaru Nakamura and Komodo developer Larry Kaufman, downplayed AlphaZero's victory, arguing that the match would have been closer if the programs had access to an opening database (since Stockfish was optimized for that scenario). Romstad additionally pointed out that Stockfish is not optimized for rigidly fixed-time moves and the version used was a year old.
Similarly, some shogi observers argued that the Elmo hash size was too low, that the resignation settings and the "EnteringKingRule" settings (cf. shogi § Entering King) may have been inappropriate, and that Elmo is already obsolete compared with newer programs.




= Reaction and criticism

=
Papers headlined that the chess training took only four hours: "It was managed in little more than the time between breakfast and lunch." Wired described AlphaZero as "the first multi-skilled AI board-game champ". AI expert Joanna Bryson noted that Google's "knack for good publicity" was putting it in a strong position against challengers. "It's not only about hiring the best programmers. It's also very political, as it helps make Google as strong as possible when negotiating with governments and regulators looking at the AI sector."
Human chess grandmasters generally expressed excitement about AlphaZero. Danish grandmaster Peter Heine Nielsen likened AlphaZero's play to that of a superior alien species. Norwegian grandmaster Jon Ludvig Hammer characterized AlphaZero's play as "insane attacking chess" with profound positional understanding. Former champion Garry Kasparov said, "It's a remarkable achievement, even if we should have expected it after AlphaGo."
Grandmaster Hikaru Nakamura was less impressed, stating: "I don't necessarily put a lot of credibility in the results simply because my understanding is that AlphaZero is basically using the Google supercomputer and Stockfish doesn't run on that hardware; Stockfish was basically running on what would be my laptop. If you wanna have a match that's comparable you have to have Stockfish running on a supercomputer as well."
Top US correspondence chess player Wolff Morrow was also unimpressed, claiming that AlphaZero would probably not make the semifinals of a fair competition such as TCEC where all engines play on equal hardware. Morrow further stated that although he might not be able to beat AlphaZero if AlphaZero played drawish openings such as the Petroff Defence, AlphaZero would not be able to beat him in a correspondence chess game either.
Motohiro Isozaki, the author of YaneuraOu, noted that although AlphaZero did comprehensively beat Elmo, the rating of AlphaZero in shogi stopped growing at a point which is at most 100–200 higher than Elmo. This gap is not that high, and Elmo and other shogi software should be able to catch up in 1–2 years.




Final results


DeepMind addressed many of the criticisms in their final version of the paper, published in December 2018 in Science. They further clarified that AlphaZero was not running on a supercomputer; it was trained using 5,000 tensor processing units (TPUs), but only ran on four TPUs and a 44-core CPU in its matches.




= Chess

=
In the final results, Stockfish 9 dev ran under the same conditions as in the TCEC superfinal: 44 CPU cores, Syzygy endgame tablebases, and a 32 GB hash size. Instead of a fixed time control of one move per minute, both engines were given 3 hours plus 15 seconds per move to finish the game. AlphaZero ran on a machine with four TPUs in addition to 44 CPU cores. In a 1000-game match, AlphaZero won with a score of 155 wins, 6 losses, and 839 draws. DeepMind also played a series of games using the TCEC opening positions; AlphaZero also won convincingly. Stockfish needed 10-to-1 time odds to match AlphaZero.




= Shogi

=
Similar to Stockfish, Elmo ran under the same conditions as in the 2017 CSA championship. The version of Elmo used was WCSC27 in combination with YaneuraOu 2017 Early KPPT 4.79 64AVX2 TOURNAMENT. Elmo operated on the same hardware as Stockfish: 44 CPU cores and a 32 GB hash size. AlphaZero won 98.2% of games when playing sente (i.e. having the first move) and 91.2% overall.




= Reactions and criticisms

=
Human grandmasters were generally impressed with AlphaZero's games against Stockfish. Former world champion Garry Kasparov said it was a pleasure to watch AlphaZero play, especially since its style was open and dynamic like his own.
In the computer chess community, Komodo developer Mark Lefler called it a "pretty amazing achievement", but also pointed out that the data was old, since Stockfish had gained a lot of strength since January 2018 (when Stockfish 8 was released). Fellow developer Larry Kaufman said AlphaZero would probably lose a match against the latest version of Stockfish, Stockfish 10, under Top Chess Engine Championship (TCEC) conditions. Kaufman argued that the only advantage of neural network–based engines was that they used a GPU, so if there was no regard for power consumption (e.g. in an equal-hardware contest where both engines had access to the same CPU and GPU) then anything the GPU achieved was "free". Based on this, he stated that the strongest engine was likely to be a hybrid with neural networks and standard alpha–beta search.
AlphaZero inspired the computer chess community to develop Leela Chess Zero, using the same techniques as AlphaZero. Leela contested several championships against Stockfish, where it showed roughly similar strength to Stockfish, although Stockfish has since pulled away.
In 2019 DeepMind published MuZero, a unified system that played excellent chess, shogi, and go, as well as games in the Atari Learning Environment, without being pre-programmed with their rules.




See also






Notes






References






External links


Chessprogramming wiki on AlphaZero
Chess.com Youtube playlist for AlphaZero vs. Stockfish

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• engines - Understanding AlphaZero - Chess Stack Exchange
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engines - Understanding AlphaZero - Chess Stack Exchange

Dec 7, 2017 · Although move 1 has the higher estimated probability but move 2 has not been searched ("low visit count" in the paper), now AlphaZero would pick move 2, and do simulation. Both moves would be considered, but AlphaZero would put more computing resources on move 1 (good thing). AlphaZero would then pick the move with the best expected outcome.

Where can you play against AlphaZero? - Chess Stack Exchange

Dec 17, 2021 · It's probably stronger than AlphaZero ever was. Their site has a page on how to play against it, either download it or play against one of the lichess bots. As a side note, stockfish has also been improving steadily (mainly with the introduction of NNUE), so it should now also be able to beat the original AlphaZero version again.

List of how AlphaZero evaluates openings - Chess Stack Exchange

Feb 20, 2018 · Finally, alphazero's principal variation for each opening is also indicated below the plots. Please see the table's caption in the paper for any other details. Overall, the English opening stands out: it kept employing it consistently throughout its training.

alphazero - Is it possible Alpha Zero will eventually solve chess ...

Nov 11, 2020 · AlphaZero "remembers" its chess performance via a neural network, which has a rather small capacity compared to the total number of chess games. So even if it were somehow possible for AlphaZero to play every possible chess game, there is no way it could remember all of them, even if you kept expanding the size of its neural network.

Which is better-Stockfish 10 or AlphaZero? - Chess Stack Exchange

Jun 1, 2020 · We can't say for sure since AlphaZero is a private engine, i.e. we don't have games between it and the latest versions of Stockfish. Still, if AlphaZero hasn't improved since it was unveiled, it will likely lose to the latest version of Stockfish. That's because AlphaZero beat Stockfish 8 by +155 = 839 -6, which is an elo difference of about 50.

Hardware used in AlphaZero vs Stockfish match

Dec 8, 2017 · AlphaZero and the previous AlphaGo Zero used a single machine with 4 TPUs Stockfish and Elmo played at their strongest skill level using 64 threads and a hash size of 1GB. So, AlphaZero used special hardware developed by Google.

How does Alphazero Respond to the QGD? - Chess Stack …

Aug 29, 2019 · There were a select of games published played between AlphaZero and Stockfish 8, see e.g. here on chess24.Some of them were played without book and some with the TCEC opening book, which I reckon led to a bigger likelihood for the QGD to occur.

What is the Elo rating of Stockfish version that played AlphaZero?

Mar 17, 2018 · AlphaZero vs. Stockfish 8, 1000-game match as in the latest paper (with Stockfish operating at full power) yielded a score of +155 -6 =839. Using this calculator gives an elo difference of 52. Stockfish 8's elo rating on computer chess rating lists is about 3378 , giving AlphaZero a rating of about 3430.

engines - The games AlphaZero lost - Chess Stack Exchange

Dec 10, 2018 · If AlphaZero were a conventional engine its developers would be looking at the openings which it lost to Stockfish, because those indicate that there's something Stockfish understands better than AlphaZero. AlphaZero is a neural network engine however, which makes how to improve it less obvious.

Knowing that AlphaZero beat Stockfish 8 (28 wins, 0 losses, and …

Feb 22, 2020 · What AlphaZero really shows is the value of othinking for yourself unless you want to be like th ecarbon-copy players who get to 2700 at age 14 and barely 2750 a decade later. Share Improve this answer