A free and strong UCI chess engine.
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Stockfish is a free and strong UCI chess engine derived from Glaurung 2.1 that analyzes chess positions and computes the optimal moves.
Stockfish does not include a graphical user interface (GUI) that is required to display a chessboard and to make it easy to input moves. These GUIs are developed independently from Stockfish and are available online. Read the documentation for your GUI of choice for information about how to use Stockfish with it.
See also the Stockfish documentation for further usage help.
This distribution of Stockfish consists of the following files:
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README.md, the file you are currently reading.
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Copying.txt, a text file containing the GNU General Public License version 3.
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AUTHORS, a text file with the list of authors for the project.
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src, a subdirectory containing the full source code, including a Makefile that can be used to compile Stockfish on Unix-like systems.
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a file with the .nnue extension, storing the neural network for the NNUE evaluation. Binary distributions will have this file embedded.
See Contributing Guide.
Improving Stockfish requires a massive amount of testing. You can donate your hardware resources by installing the Fishtest Worker and viewing the current tests on Fishtest.
In the chessprogramming wiki, many techniques used in Stockfish are explained with a lot of background information. The section on Stockfish describes many features and techniques used by Stockfish. However, it is generic rather than focused on Stockfish's precise implementation.
The engine testing is done on Fishtest. If you want to help improve Stockfish, please read this guideline first, where the basics of Stockfish development are explained.
Discussions about Stockfish take place these days mainly in the Stockfish Discord server. This is also the best place to ask questions about the codebase and how to improve it.
The Universal Chess Interface (UCI) is a standard text-based protocol used to communicate with a chess engine and is the recommended way to do so for typical graphical user interfaces (GUI) or chess tools. Brainlearn implements the majority of its options.
Developers can see the default values for the UCI options available in Brainlearn
by typing ./brainlearn uci in a terminal, but most users should typically use a
chess GUI to interact with Brainlearn.
For more information on UCI or debug commands, see our [documentation][wiki-commands-link].
BrainLearn hash the same options as BrainFish, but it implements a persisted learning algorithm, managing a file named experience.exp.
It is a collection of one or more positions stored with the following format (similar to in memory Brainlearn Transposition Table):
- best move
- board signature (hash key)
- best move depth
- best move score
- best move performance , a new parameter you can calculate with any learning application supporting this specification. An example is the private one, kernel of SaaS part of Alpha-Chess AI portal. The idea is to calculate it based on pattern recognition concept. In the portal, you can also exploit the reports of another NLG (virtual trainer) application and buy the products in the digishop based on all this. This open-source part has the performance default. So, it doesn't use it. Clearly, even if already strong, this private learning algorithm is a lot stronger as demostrate here: Graphical result
This file is loaded in an hashtable at the engine load and updated each time the engine receive quit or stop uci command. When BrainLearn starts a new game or when we have max 8 pieces on the chessboard, the learning is activated and the hash table updated each time the engine has a best score at a depth >= 4 PLIES, according to Brainlearn aspiration window.
At the engine loading, there is an automatic merge to experience.exp files, if we put the other ones, based on the following convention:
<fileType><qualityIndex>.exp
where
- fileType=experience
- qualityIndex , an integer, incrementally from 0 on based on the file's quality assigned by the user (0 best quality and so on)
N.B.
Because of disk access, less time the engine can think, less effective is the learning.
Old versions had this experience file with a .bin extension, but now we added the bin book format support, so the extension is changed in .exp. So, old files can simply be renamed by changing this extension.
The default value is 0 and keep it for analysis purpose. For game playing, you can use the default brainlearn value 24
Boolean, Default: True For match play, activate it and the engine uses the dynamic contempt. For analysis purpose; keep it at its default, to completely avoid, with contempt settled to 0, the well known rollercoaster effect and align so the engine's score to the gui's informator symbols
Boolean, Default: False If activated, the learning file is only read.
Boolean, Default: False If activated, the learning algorithm is the Q-learning, optimized for self play. Some GUIs don't write the experience file in some game's modes because the uci protocol is differently implemented
Boolean, Default: False If activated, the engine uses the MonteCarlo Tree Search in the manner specified by the following parameters.
Integer, Default: 0, Min: 0, Max: 512 The number of settled threads to use for MCTS search except the first (main) one always for alpha-beta search. In particular, if the number is greater than threads number, they will all do a montecarlo tree search, always except the first (main) for alpha-beta search.
Integer, Default: 20, Min: 0, Max: 100 Only in multi mcts mode, for tree policy.
Integer, Default: 5, Min: 0, Max: 1000 Only in multi mcts mode, for Upper Confidence Bound.
The order is: bin->ctg->live book
The file name of the first book file which could be a polyglot (BIN) or Chessbase (CTG) book. To disable this book, use: <empty>
If the book (CTG or BIN) is in a different directory than the engine executable, then configure the full path of the book file, example:
C:\Path\To\My\Book.ctg or /home/username/path/to/book/bin
The number of moves to consider from the book for the same position. To play best book move, set this option to 1. If a value n (greater than 1) is configured, the engine will pick randomly one of the top n moves available in the book for the given position
The maximum number of moves to play from the book
This option is only used if the loaded book is a CTG book. When set to true, the engine will only play Green moves from the book (if any). If no green moves found, then no book move is made
This option has no effect or use if the loaded book is a Polyglot (BIN) book
Same explaination as CTG/BIN Book 1 File, but for the second book
Same explaination as BIN Book 1 Width, but for the second book
Same explaination as BIN Book 1 Depth, but for the second book
Same explaination as (CTG) Book 1 Only Green, but for the second book
Polyfish supports all UCI commands supported by Brainlearn. Click here to see the full list of supported Brainlearn UCI commands
Polyfish also supports the following UCI commands
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This command causes the engine to show available moves and associated information from the currently configured books
position startpos poly +---+---+---+---+---+---+---+---+ | r | n | b | q | k | b | n | r | 8 +---+---+---+---+---+---+---+---+ | p | p | p | p | p | p | p | p | 7 +---+---+---+---+---+---+---+---+ | | | | | | | | | 6 +---+---+---+---+---+---+---+---+ | | | | | | | | | 5 +---+---+---+---+---+---+---+---+ | | | | | | | | | 4 +---+---+---+---+---+---+---+---+ | | | | | | | | | 3 +---+---+---+---+---+---+---+---+ | P | P | P | P | P | P | P | P | 2 +---+---+---+---+---+---+---+---+ | R | N | B | Q | K | B | N | R | 1 +---+---+---+---+---+---+---+---+ a b c d e f g h Fen: rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq - 0 1 Key: 8F8F01D4562F59FB Checkers: Polyglot book 1: MyNarrowBook.bin 1 : e2e4 , count: 8822 2 : d2d4 , count: 6644 Polyglot book 2: MyWideBook.bin 1 : e2e4 , count: 9768 2 : d2d4 , count: 5347 3 : g1f3 , count: 1034 4 : c2c4 , count: 965 5 : b2b3 , count: 99 6 : f2f4 , count: 94 7 : g2g3 , count: 76 8 : b2b4 , count: 43 9 : e2e3 , count: 32 10: b1c3 , count: 32 11: d2d3 , count: 13 12: c2c3 , count: 12 13: a2a3 , count: 10 14: g2g4 , count: 9 15: h2h3 , count: 3 16: h2h4 , count: 3 17: a2a4 , count: 1 18: g1h3 , count: 1 19: b1a3 , count: 1 20: f2f3 , count: 1
CTG book format specification is not available to the public from Chessbase. The code that reads and parses CTG books is based on the reverse engineered book specification published on CTG Specifications as well as the other resources mentioned earlier.
The reverse engineered specs are good enough to proble the book for moves, but it does not provide the same functionality as Chessbase own products. The following is a list of known limitations of Polyfish's CTG book implementation:
- Does not support underpromotion, which means all promotion moves are assumed to be to a Queen
- Does not support more than two Queens on the board
- The logic that determines Green/Red moves is not 100% accurate which may cause the engine not to play certain Green moves because it cannot identify them.
- Some move annotations and engine recommendations can be read.
The move weight calculated by Polyfish (can be seen using the book command) is my own attempt to compensate for all the missing/unknown information about the CTG specification. Using the calculated weight, Polyfish can pick the best move for a given position in 99% of the times because it ustilizes existing move statistics such as number of wins, draws, and losses, as well as "known" annotations (!, !?, ?!, ??, OnlyMove, etc...) and recommendations (Green vs. Red) if available.
Despite the fact that Polyfish can read and play from CTG Book, it is not going to be identical to Chessbase own products since it is based on the partial CTG specification available publicly. Use at your own risk!
Default is Off: no livebook. The other values are "NoEgtbs" (no livebook for an endgame with at max 7 mens), "Egtbs" (livebook only for an endgame with at max 7 mens) and "Both" (the livebook whenever is possible).
The default is the online chessdb https://www.chessdb.cn/queryc_en/, a wonderful project by noobpwnftw (thanks to him!)
https://github.com/noobpwnftw/chessdb http://talkchess.com/forum3/viewtopic.php?f=2&t=71764&hilit=chessdb
The private application can also learn from this live db.
Default 5000, min 0, max 10000 Only for bullet games, use a lower value, for example, 1500.
Default 3, min 1, max 100 Max times the engine tries to contribute (if the corresponding option is activated: see below) to the live book. If 0, the engine doesn't use the livebook.
Boolean, Default: False If activated, the engine varies its play, reducing conversely its strength because already the live chessdb is very large.
Boolean, Default: False If activated, the engine sends a move, not in live chessdb, in its queue to be analysed. In this manner, we have a kind of learning cloud.
Default 255, min 1, max 255 Depth of live book moves.
Integer, Default: 0, Min: 0, Max: 40 To play different opening lines from default (0), if not from book (see below). Higher variety -> more probable loss of ELO
Boolean, Default: False Set this option to true when running under CuteChess and you experiences problems with concurrency > 1 When this option is true, the saved experience file name will be modified to something like experience-64a4c665c57504a4.exp (64a4c665c57504a4 is random). Each concurrent instance of BrainLearn will have its own experience file name, however, all the concurrent instances will read "experience.exp" at start up.
Converting pgn to brainlearn format is really simple.
Requirements
- Download cuteChess gui
- Download brainlearn
- Download brainlearn or equivalent
- Download the pgn files you want to convert
In Cute Chess, set a tournament according to the photo in this link: CuteChess Settings
- Add the 2 engines (One should be brainlearn)
-start a tournament with brainlearn and any other engine. It will convert all the games in the pgn file and save them to game.bin
Note: We recommend you use games from high quality play.
Brainlearn has support for 32 or 64-bit CPUs, certain hardware instructions, big-endian machines such as Power PC, and other platforms.
On Unix-like systems, it should be easy to compile Brainlearn directly from the
source code with the included Makefile in the folder src. In general, it is
recommended to run make help to see a list of make targets with corresponding
descriptions. An example suitable for most Intel and AMD chips:
cd src
make -j profile-build ARCH=x86-64-avx2
Detailed compilation instructions for all platforms can be found in our documentation. Our wiki also has information about the UCI commands supported by Brainlearn.
Brainlearn is free and distributed under the GNU General Public License version 3 (GPL v3). Essentially, this means you are free to do almost exactly what you want with the program, including distributing it among your friends, making it available for download from your website, selling it (either by itself or as part of some bigger software package), or using it as the starting point for a software project of your own.
The only real limitation is that whenever you distribute Brainlearn in some way, you MUST always include the license and the full source code (or a pointer to where the source code can be found) to generate the exact binary you are distributing. If you make any changes to the source code, these changes must also be made available under GPL v3.