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This paper uses chess, a landmark planning problem in AI, to assess transformers' performance on a planning task where memorization is futile $\unicode{x2013}$ even at a large scale.
A new measure of rank correlation
Maurice George Kendall · 1938
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Cyril Burt · 1955
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Steven J. Edwards · 1994
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Sebastian Thrun · 1994
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Rudolf Huber and Stefan Meyer-Kahlen · 2000
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Deep blue
Murray Campbell, A. Joseph Hoane Jr., and Feng-Hsiung Hsu · 2002
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Whole-history rating: A bayesian rating system for players of time-varying strength
Rémi Coulom · 2008
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Stockfish, 2008
Tord Romstad, Marco Costalba, Joona Kiiski, Gary Linscott, Yu Nasu, Motohiro Isozaki, Hisayori Noda, and et al · 2008
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Bootstrapping from game tree search
Joel Veness, David Silver, William T. B. Uther, and Alan Blair · 2009
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Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin A. Riedmiller, Andreas Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, and Demis Hassabis · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Giraffe: Using deep reinforcement learning to play chess
Matthew Lai · 2015
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Move evaluation in go using deep convolutional neural networks
Chris J. Maddison, Aja Huang, Ilya Sutskever, and David Silver · 2015
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Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Vedavyas Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy P. Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, and Demis Hassabis · 2016
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Deepchess: End-to-end deep neural network for automatic learning in chess
Omid E. David, Nathan S. Netanyahu, and Lior Wolf · 2016
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Mastering chess and shogi by self-play with a general reinforcement learning algorithm
David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, Timothy P. Lillicrap, Karen Simonyan, and Demis Hassabis · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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Convolutional sequence to sequence learning
Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, and Yann N. Dauphin · 2017
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LeelaChessZero, 2018
The LCZero Authors · 2018
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Efficiently updatable neural-network-based evaluation functions for computer shogi
Yu Nasu · 2018
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Improving regression performance with distributional losses
Ehsan Imani and Martha White · 2018
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Deep pepper: Expert iteration based chess agent in the reinforcement learning setting
Sai Krishna G. V., Kyle Goyette, Ahmad Chamseddine, and Breandan Considine · 2018
Haiku: Sonnet for JAX, 2020
Tom Hennigan, Trevor Cai, Tamara Norman, Lena Martens, and Igor Babuschkin · 2020
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Watching a language model learning chess
Andreas Stöckl · 2021
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Dominik Klein · 2022
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Learning models of individual behavior in chess
Reid McIlroy-Young, Russell Wang, Siddhartha Sen, Jon M. Kleinberg, and Ashton Anderson · 2022
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Learning chess with language models and transformers
Michael DeLeo and Erhan Guven · 2022
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Chess as a testbed for language model state tracking
Shubham Toshniwal, Sam Wiseman, Karen Livescu, and Kevin Gimpel · 2022
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lichess-bot, 2018
Ioannis Pantidis · 2018
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JAX: composable transformations of Python+NumPy programs, 2018
James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, George Necula, Adam Paszke, Jake VanderPlas, Skye Wanderman-Milne, and Qiao Zhang · 2018
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Mish: A self regularized non-monotonic neural activation function
Diganta Misra · 2019
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Sentimate: Learning to play chess through natural language processing
Isaac Kamlish, Isaac Bentata Chocron, and Nicholas McCarthy · 2019
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Game Changer: AlphaZero’s Groundbreaking Chess Strategies and the Promise of AI
Matthew Sadler and Natasha Regan · 2019
Cited alongside, same era.
Artificial Intelligence: A Modern Approach (4th Edition)
Stuart Russell and Peter Norvig · 2020
Cited alongside, same era.
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Playing chess with large language models, 2023
Nicholas Carlini · 2023
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Distributional Reinforcement Learning
Marc G. Bellemare, Will Dabney, and Mark Rowland · 2023
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Exact ratings for everyone on lichess, 2023
Justas Zabulionis · 2023
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Improving the strength of human-like models in chess, 2023
Saumik Narayanan, Kassa Korley, Chien-Ju Ho, and Siddhartha Sen · 2023
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Exploring GPT’s capabilities in chess-puzzles
Borja Albert Gramaje · 2023
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Representation matters: The game of chess poses a challenge to vision transformers
Johannes Czech, Jannis Blüml, and Kristian Kersting · 2023
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Haifa Alrdahi and Riza Batista-Navarro · 2023
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Chessgpt: Bridging policy learning and language modeling
Xidong Feng, Yicheng Luo, Ziyan Wang, Hongrui Tang, Mengyue Yang, Kun Shao, David Mguni, Yali Du, and Jun Wang · 2023
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Mastering chess with a transformer model
Daniel Monroe and The Leela Chess Zero Team · 2024
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Stop regressing: Training value functions via classification for scalable deep RL
Jesse Farebrother, Jordi Orbay, Quan Vuong, Adrien Ali Taïga, Yevgen Chebotar, Ted Xiao, Alex Irpan, Sergey Levine, Pablo Samuel Castro, Aleksandra Faust, Aviral Kumar, and Rishabh Agarwal · 2024
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The illusion of state in state-space models
William Merrill, Jackson Petty, and Ashish Sabharwal · 2024
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