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'''ShashChess''',<br/>
a [[Stockfish]] based [[:Category:Derivative|derivative]] by [[Andrea Manzo]] with the aim to apply the proposals of [[Alexander Shashin]] as exposed in his book ''Best Play: A New Method for Discovering the Strongest Move'' <ref>[http://www.bs-chess.com/latin/lectures/2011/shashin4.html Welcome to BS Chess]</ref> <ref>[[Alexander Shashin]] ('''2013'''). ''Best Play: A New Method for Discovering the Strongest Move''. [https://mongoosepress.com/ Mongoose Press], [https://www.amazon.com/Best-Play-Discovering-Strongest-2013-01-01/dp/B01A0CKJQM Amazon]</ref>
<ref>[https://web.archive.org/web/20130909054429/http://www.chessvibes.com/review-best-play Review: Best Play | ChessVibes] by [https://ratings.fide.com/profile/1005820 Arne Moll], September 05, 2013 ([https://en.wikipedia.org/wiki/Wayback_Machine Wayback Machine])</ref>.
First released in July 2018 <ref>[http://www.talkchess.com/forum3/viewtopic.php?f=2&t=68093 ShashChess] by [[Andrea Manzo]], [[CCC]], July 28, 2018</ref>,
subsequent ShashChess versions feature [[Learning|learning]] by utilizing a [[Persistent Hash Table|persistent hash table]], [[Playing Strength|skill levels]] and handicap modes, [[NNUE]] and , [[Monte-Carlo Tree Search]] with one or multiple [[Thread|threads]] in conjunction with [[Alpha-Beta|alpha-beta]],and various [[Learning|learning]] techniques utilizing a [[Persistent Hash Table|persistent hash table]] <ref>[https://github.com/amchess/ShashChess/blob/master/README.md ShashChess/README.md at master · amchess/ShashChess · GitHub]</ref> <ref>[http://www.talkchess.com/forum3/viewtopic.php?f=2&t=6809370948&start=265 17 Re: ShashChessKomodo MCTS] by [[Eduard NemethMark Lefler]], [[CCC]], May 20June 12, 20212019 » [[Komodo#MCTS|Komodo MCTS]]</ref>.
=Personalities=
</pre>
These personalities are considered in various [[Selectivity|search selectivity]] thresholds, along with multiple dynamic evaluation score adjustments.
=Q-Learning=
A [https://en.wikipedia.org/wiki/Rote_learning rote learning] technique inspired from [[Reinforcement Learning#Q-Learning|Q-learning]], worked out and introduced by [[Kelly Kinyama]]
<ref>[https://groups.google.com/g/fishcooking/c/fhX7dFAsyew/m/NSd0-OJjBwAJ Re: Self-Learning stockfish upgraded] by [[Kelly Kinyama]], [[Computer Chess Forums|FishCooking]], May 28, 2019</ref>
<ref>[https://groups.google.com/g/fishcooking/c/6IzmiSCB8lg/m/sFeSq9ykAQAJ A new reinforcement learning implementation of Q learning algorithm for alphabeta engines to automatically tune the evaluation of chess positions] by [[Kelly Kinyama]], [[Computer Chess Forums|FishCooking]], June 29, 2020</ref>
and also employed in [[BrainLearn]] 9.0 <ref>[https://github.com/amchess/BrainLearn/releases/tag/9.0 Release BrainLearn 9.0 · amchess/BrainLearn · GitHub]</ref>,
was applied in ShashChess since version 12.0 <ref>[https://groups.google.com/g/fishcooking/c/GLag32ARtKo/m/3Zoaq3-rAwAJ ShashChess 12.0] by [[Andrea Manzo]], [[Computer Chess Forums|FishCooking]], June 28, 2020</ref>.
After the end of a decisive selfplay game, the [[Move List|list of moves]] (ml) and associated [[Score|scores]] is merged into the learn table from end to start,
the score of timestep t adjusted as weighted average with the future reward of timestep t+1, using a [https://en.wikipedia.org/wiki/Q-learning#Learning_Rate learning rate] α of 0.5 and a [https://en.wikipedia.org/wiki/Q-learning#Discount_factor discount factor] γ of 0.99 <ref>[https://github.com/amchess/ShashChess/blob/master/src/All/search.cpp#L2625 ShashChess/search.cpp at master · amchess/ShashChess · GitHub]</ref>:
<pre>
for (t = ml.size() - 2; t >= 0; t--) {
ml[t].score = (1-α)*ml[t].score + α*γ*ml[t+1].score;
insertIntoOrUpdateLearningTable( ml[t] );
}
</pre>
During repeated selfplay games, subsequently playing along the learned best line so far, decreasing score adjustments will stimulate exploration of alternative siblings, while increasing score adjustments correspondents to exploitation of the best move.
=Forum Posts=
==2018 ...==
* [http://www.talkchess.com/forum3/viewtopic.php?f=2&t=68093 ShashChess] by [[Andrea Manzo]], [[CCC]], July 28, 2018
: [http://www.talkchess.com/forum3/viewtopic.php?f=2&t=68093&start=103 Re: ShashChess] (11.0) by [[Andrea Manzo]], [[CCC]], March 06, 2020
: [http://www.talkchess.com/forum3/viewtopic.php?f=2&t=68093&start=105 Re: ShashChess] (12.0) by [[Andrea Manzo]], [[CCC]], June 28, 2020
: [http://www.talkchess.com/forum3/viewtopic.php?f=2&t=68093&start=209 Re: ShashChess] (15.0) by [[Andrea Manzo]], [[CCC]], October 03, 2020
: [http://www.talkchess.com/forum3/viewtopic.php?f=2&t=68093&start=272 Re: ShashChess] (17.1) by [[Andrea Manzo]], [[CCC]], June 01, 2021
* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=68120&p=769919 Build ShashChess for Android] by [[Andrea Manzo]], [[CCC]], August 01, 2018
==2020 ...==
* [https://groups.google.com/g/fishcooking/c/GLag32ARtKo/m/3Zoaq3-rAwAJ ShashChess 12.0] by [[Andrea Manzo]], [[Computer Chess Forums|FishCooking]], June 28, 2020
* [https://groups.google.com/g/fishcooking/c/6IzmiSCB8lg/m/sFeSq9ykAQAJ A new reinforcement learning implementation of Q learning algorithm for alphabeta engines to automatically tune the evaluation of chess positions] by [[Kelly Kinyama]], [[Computer Chess Forums|FishCooking]], June 29, 2020
* [https://groups.google.com/d/msg/fishcooking/yWtpz_FY5_Y/RMTG56fkAAAJ ShashChess NNUE 1.0] by [[Andrea Manzo]], [[Computer Chess Forums|FishCooking]], July 25, 2020
* [http://www.talkchess.com/forum3/viewtopic.php?f=2&t=76394 Shashchess which executable to use] by Andrew Bernasrd, [[CCC]], January 23, 2021
* [https://groups.google.com/g/fishcooking/c/Iy1AlEZJWc8 New BrainLearn and ShashChess] by [[Andrea Manzo]], [[Computer Chess Forums|FishCooking]], May 19, 2021
=External Links=
