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playing-games

Game of Go

3260 papers • 126 benchmarks • 313 datasets

Go is an abstract strategy board game for two players, in which the aim is to surround more territory than the opponent. The task is to train an agent to play the game and be superior to other players.

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Benchmarks

These leaderboards are used to track progress in game-of-go

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Dataset

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ELO Ratings

ELO Ratings

Libraries

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Use these libraries to find game-of-go models and implementations

opendilab/LightZero

4 papers 855

Datasets

PAGE

Subtasks

No subtasks available.

Most implemented papers

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

K. Simonyan, Julian Schrittwieser, T. Hubert, D. Hassabis, L. Sifre, David Silver, T. Lillicrap, A. Guez, Ioannis Antonoglou, D. Kumaran, Marc Lanctot, T. Graepel, Matthew Lai•Mon Dec 04 2017

This paper generalises the approach into a single AlphaZero algorithm that can achieve, tabula rasa, superhuman performance in many challenging domains, and convincingly defeated a world-champion program in each case.

2005

Content

Introduction Benchmarks Datasets Subtasks Libraries Papers

QueensGambit/CrazyAra

2 papers 229

0

Better Computer Go Player with Neural Network and Long-term Prediction

Yuandong Tian, Yan Zhu•Wed Nov 18 2015

Against human players, the newest versions, darkfores2, achieve a stable 3d level on KGS Go Server as a ranked bot, a substantial improvement upon the estimated 4k-5k ranks for DCNN reported in Clark & Storkey (2015) based on games against other machine players.

86 0

Accelerating Self-Play Learning in Go

David J. Wu•Tue Feb 26 2019

By introducing several improvements to the AlphaZero process and architecture, this work greatly accelerate self-play learning in Go, achieving a 50x reduction in computation over comparable methods.

106 0

Planning in Stochastic Environments with a Learned Model

Julian Schrittwieser, David Silver, Ioannis Antonoglou, Sherjil Ozair, Thomas K. Hubert•Fri Dec 31 2021

A new algorithm is introduced, Stochastic MuZero, that learns a stochastic model incorporating afterstates, and uses this model to perform a stochastic tree search and maintain the superhuman performance of standard MuZero in the game of Go.

73 0

Mastering Atari, Go, chess and shogi by planning with a learned model

K. Simonyan, Julian Schrittwieser, T. Hubert, D. Hassabis, L. Sifre, David Silver, T. Lillicrap, A. Guez, Ioannis Antonoglou, T. Graepel, Edward Lockhart, Simon Schmitt•Mon Nov 18 2019

The MuZero algorithm is presented, which, by combining a tree-based search with a learned model, achieves superhuman performance in a range of challenging and visually complex domains, without any knowledge of their underlying dynamics.

2350 0

Teaching Deep Convolutional Neural Networks to Play Go

A. Storkey, Christopher Clark•Tue Dec 09 2014

Convolutional neural networks trained to play Go are able to consistently defeat the well known Go program GNU Go, indicating it is state of the art among programs that do not use Monte Carlo Tree Search.

32 0

FML-based Dynamic Assessment Agent for Human-Machine Cooperative System on Game of Go

Chang-Shing Lee, Mei-Hui Wang, Sheng-Chi Yang, Pi-Hsia Hung, Su-Wei Lin, Nan Shuo, N. Kubota, Chun-Hsun Chou, Ping-Chiang Chou, Chia-Hsiu Kao•Sat Jul 15 2017

The experimental results demonstrate that the proposed FDAA can work effectively for Go applications and an FML-based Human-Machine Cooperative System for the game of Go.

11 0

ELF OpenGo: An Analysis and Open Reimplementation of AlphaZero

Shubho Sengupta, James Pinkerton, C. L. Zitnick, Yuandong Tian, Zhuoyuan Chen, Jerry Ma, Qucheng Gong•Mon Feb 11 2019

ELF OpenGo is the first open-source Go AI to convincingly demonstrate superhuman performance with a perfect (20:0) record against global top professionals and is proposed, anopen-source reimplementation of the AlphaZero algorithm.

116 0

MoËT: Mixture of Expert Trees and its application to verifiable reinforcement learning

Rishabh Singh, Marko Vasic, A. Petrović, Kaiyuan Wang, Mladen Nikolic, S. Khurshid•Sat Jun 15 2019

MoËT, a novel model based on Mixture of Experts, consisting of decision tree experts and a generalized linear model gating function, which is more expressive than the standard decision tree and can be used in real-world supervised problems on which it outperforms other verifiable machine learning models.

26 0

Adding a benchmark result helps the community track progress.

Game of Go | State-of-the-Art