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Larger and deeper network models will improve the receptivity, the amount of knowledge and pattern to extract from the training samples, with potential for a [[Playing Strength|stronger]] engine.
As a further improvement, Leele Chess Zero applies the ''Squeeze and Excite'' (SE) extension to the residual block architecture <ref>[https://github.com/LeelaChessZero/lc0/wiki/Technical-Explanation-of-Leela-Chess-Zero Technical Explanation of Leela Chess Zero · LeelaChessZero/lc0 Wiki · GitHub]</ref> <ref>[https://towardsdatascience.com/squeeze-and-excitation-networks-9ef5e71eacd7 Squeeze-and-Excitation Networks – Towards Data Science] by [http://plpp.de/ Paul-Louis Pröve], October 17, 2017</ref>.
The body is fully connected to both the policy "head" for the move probability distribution, and value "head" for the evaluation score aka [[Pawn Advantage, Win Percentage, and Elo|winning probability]] of the the the current positions position and up to seven predecessor positions on the input planes.
==Training==
Like in [[AlphaZero]], the '''Zero''' suffix implies no other initial knowledge than the rules of the gamesgame, to build a superhuman player, starting with truly random self-play games to apply [[Reinforcement Learning|reinforcement learning]] based on the outcome of that games.
However, there are derived approaches, such as [[Albert Silver|Albert Silver's]] [[Deus X]], trying to take a short-cut by initially using [[Supervised Learning|supervised learning]] techniques, such as feeding in high quality games played by other strong chess playing entities, or huge records of positions with a given preferred move.
The unsupervised training of the NN is about to minimize the [https://en.wikipedia.org/wiki/Norm_(mathematics)#Euclidean_norm L2-norm] of the [https://en.wikipedia.org/wiki/Mean_squared_error mean squared error] loss of the value output and the policy loss. Further there are experiments to train the value head against not the game outcome, but against the accumulated value for a position after exploring some number of nodes with [[UCT]] <ref>[https://medium.com/oracledevs/lessons-from-alphazero-part-4-improving-the-training-target-6efba2e71628 Lessons From AlphaZero (part 4): Improving the Training Target] by [https://blogs.oracle.com/author/vish-abrams Vish Abrams], [https://blogs.oracle.com/ Oracle Blog], June 27, 2018</ref>.
* [[AlphaZero]]
* [[Leela Zero]]
* [[Leila]]
* [[Deep Learning]]
* [[Monte-Carlo Tree Search]]
==Chess Engine==
* [https://en.wikipedia.org/wiki/Leela_Chess_Zero Leela Chess Zero from Wikipedia]
* [https://en.wikipedia.org/wiki/Leela_(software) Leela (software) from Wikipedia]
* [http://lczero.org/ LCZero]
* [https://github.com/LeelaChessZero/lczero GitHub - LeelaChessZero/lczero: A chess adaption of GCP's Leela Zero]
* [https://en.chessbase.com/post/leela-chess-zero-alphazero-for-the-pc Leela Chess Zero: AlphaZero for the PC] by [[Albert Silver]], [[ChessBase|ChessBase News]], April 26, 2018
==Misc==
* [https://en.wikipedia.org/wiki/Leela Leela from Wikipedia]
* [https://en.wikipedia.org/wiki/Leela_(game) Leela (game) from Wikipedia]
* [https://en.wikipedia.org/wiki/Leela_(name) Leela (name) from Wikipedia]
* [https://en.wikipedia.org/wiki/Leela_(Doctor_Who) Leela (Doctor Who) from Wikipedia]
* [https://en.wikipedia.org/wiki/Leela_(Futurama) Leela (Futurama) from Wikipedia]
* [[:Category:Marc Ribot|Marc Ribot's]] Ceramic Dog - Lies My Body Told Me (Live on [https://en.wikipedia.org/wiki/KEXP-FM KEXP], July 20, 2016), [https://en.wikipedia.org/wiki/YouTube YouTube] Video
[[Category:GPL]]
[[Category:Marc Ribot]]
[[Category:Fiction]]
[[Category:Given Name]]
