Methods > Reinforcement Learning > Board Game Models

AlphaZero

Introduced by Silver et al. in Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
 
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AlphaZero is a reinforcement learning agent for playing board games such as Go, chess, and shogi.

Source: Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
Paper

Latest Papers

PAPER DATE
Assessing Game Balance with AlphaZero: Exploring Alternative Rule Sets in Chess
Nenad Tomašev • Ulrich Paquet • Demis Hassabis • Vladimir Kramnik
2020-09-09
Learning Personalized Models of Human Behavior in Chess
Reid McIlroy-Young • Russell Wang • Siddhartha Sen • Jon Kleinberg • Ashton Anderson
2020-08-23
Learning Compositional Neural Programs for Continuous Control
Thomas Pierrot • Nicolas Perrin • Feryal Behbahani • Alexandre Laterre • Olivier Sigaud • Karim Beguir • Nando de Freitas
2020-07-27
Combining Deep Reinforcement Learning and Search for Imperfect-Information Games
Noam Brown • Anton Bakhtin • Adam Lerer • Qucheng Gong
2020-07-27
Monte-Carlo Tree Search as Regularized Policy Optimization
Jean-Bastien Grill • Florent Altché • Yunhao Tang • Thomas Hubert • Michal Valko • Ioannis Antonoglou • Rémi Munos
2020-07-24
Convex Regularization in Monte-Carlo Tree Search
Tuan Dam • Carlo D'Eramo • Jan Peters • Joni Pajarinen
2020-07-01
Aligning Superhuman AI and Human Behavior: Chess as a Model System
| Reid McIlroy-Young • Siddhartha Sen • Jon Kleinberg • Ashton Anderson
2020-06-02
Think Too Fast Nor Too Slow: The Computational Trade-off Between Planning And Reinforcement Learning
Thomas M. Moerland • Anna Deichler • Simone Baldi • Joost Broekens • Catholijn M. Jonker
2020-05-15
Neural Machine Translation with Monte-Carlo Tree Search
| Jerrod Parker • Jerry Zikun Chen
2020-04-27
Warm-Start AlphaZero Self-Play Search Enhancements
Hui Wang • Mike Preuss • Aske Plaat
2020-04-26
Approximate exploitability: Learning a best response in large games
Finbarr Timbers • Edward Lockhart • Marc Lanctot • Martin Schmid • Julian Schrittwieser • Thomas Hubert • Michael Bowling
2020-04-20
Accelerating and Improving AlphaZero Using Population Based Training
Ti-Rong Wu • Ting-Han Wei • I-Chen Wu
2020-03-13
Learning to Resolve Alliance Dilemmas in Many-Player Zero-Sum Games
Edward Hughes • Thomas W. Anthony • Tom Eccles • Joel Z. Leibo • David Balduzzi • Yoram Bachrach
2020-02-27
Polygames: Improved Zero Learning
Tristan Cazenave • Yen-Chi Chen • Guan-Wei Chen • Shi-Yu Chen • Xian-Dong Chiu • Julien Dehos • Maria Elsa • Qucheng Gong • Hengyuan Hu • Vasil Khalidov • Cheng-Ling Li • Hsin-I Lin • Yu-Jin Lin • Xavier Martinet • Vegard Mella • Jeremy Rapin • Baptiste Roziere • Gabriel Synnaeve • Fabien Teytaud • Olivier Teytaud • Shi-Cheng Ye • Yi-Jun Ye • Shi-Jim Yen • Sergey Zagoruyko
2020-01-27
Three-Head Neural Network Architecture for AlphaZero Learning
Anonymous
2020-01-01
Self-Play Learning Without a Reward Metric
Dan Schmidt • Nick Moran • Jonathan S. Rosenfeld • Jonathan Rosenthal • Jonathan Yedidia
2019-12-16
Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model
| Julian Schrittwieser • Ioannis Antonoglou • Thomas Hubert • Karen Simonyan • Laurent Sifre • Simon Schmitt • Arthur Guez • Edward Lockhart • Demis Hassabis • Thore Graepel • Timothy Lillicrap • David Silver
# 1 ATARI GAMES ON ATARI 2600 ROBOTANK
2019-11-19
Multiplayer AlphaZero
| Nick Petosa • Tucker Balch
2019-10-29
Exploring the Performance of Deep Residual Networks in Crazyhouse Chess
| Sun-Yu Gordon Chi
2019-08-25
Performing Deep Recurrent Double Q-Learning for Atari Games
Felipe Moreno-Vera
2019-08-16
Multiple Policy Value Monte Carlo Tree Search
Li-Cheng Lan • Wei Li • Ting-Han Wei • I-Chen Wu
2019-05-31
Learning Compositional Neural Programs with Recursive Tree Search and Planning
Thomas Pierrot • Guillaume Ligner • Scott Reed • Olivier Sigaud • Nicolas Perrin • Alexandre Laterre • David Kas • Karim Beguir • Nando de Freitas
2019-05-30
Deep Policies for Width-Based Planning in Pixel Domains
| Miquel Junyent • Anders Jonsson • Vicenç Gómez
2019-04-12
Improved Reinforcement Learning with Curriculum
Joseph West • Frederic Maire • Cameron Browne • Simon Denman
2019-03-29
Hyper-Parameter Sweep on AlphaZero General
| Hui Wang • Michael Emmerich • Mike Preuss • Aske Plaat
2019-03-19
α-Rank: Multi-Agent Evaluation by Evolution
Shayegan Omidshafiei • Christos Papadimitriou • Georgios Piliouras • Karl Tuyls • Mark Rowland • Jean-Baptiste Lespiau • Wojciech M. Czarnecki • Marc Lanctot • Julien Perolat • Remi Munos
2019-03-04
Accelerating Self-Play Learning in Go
| David J. Wu
2019-02-27
ELF OpenGo: An Analysis and Open Reimplementation of AlphaZero
| Yuandong Tian • Jerry Ma • Qucheng Gong • Shubho Sengupta • Zhuoyuan Chen • James Pinkerton • C. Lawrence Zitnick
2019-02-12
The Entropy of Artificial Intelligence and a Case Study of AlphaZero from Shannon's Perspective
Bo Zhang • Bin Chen • Jin-lin Peng
2018-12-14
Assessing the Potential of Classical Q-learning in General Game Playing
| Hui Wang • Michael Emmerich • Aske Plaat
2018-10-14
ExIt-OOS: Towards Learning from Planning in Imperfect Information Games
| Andy Kitchen • Michela Benedetti
2018-08-30
Ranked Reward: Enabling Self-Play Reinforcement Learning for Combinatorial Optimization
| Alexandre Laterre • Yunguan Fu • Mohamed Khalil Jabri • Alain-Sam Cohen • David Kas • Karl Hajjar • Torbjorn S. Dahl • Amine Kerkeni • Karim Beguir
2018-07-04
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 Lillicrap • Karen Simonyan • Demis Hassabis
# 1 GAME OF SHOGI ON ELO RATINGS
2017-12-05

Tasks

TASK PAPERS SHARE
Game of Go 5 19.23%
Atari Games 4 15.38%
Decision Making 4 15.38%
Board Games 4 15.38%
Game of Chess 2 7.69%
Game of Shogi 2 7.69%
Machine Translation 1 3.85%
Multi-agent Reinforcement Learning 1 3.85%
Mathematical Proofs 1 3.85%

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