About
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.
Benchmarks
| TREND | DATASET | BEST METHOD | PAPER TITLE | PAPER | CODE | COMPARE |
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Greatest papers with code
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
The game of chess is the most widely-studied domain in the history of artificial intelligence.
Ranked #1 on
Game of Go
on ELO Ratings
GAME OF CHESS GAME OF GO GAME OF SHOGI GENERAL REINFORCEMENT LEARNING
ELF OpenGo: An Analysis and Open Reimplementation of AlphaZero
• pytorch/ELF
• 
The AlphaGo, AlphaGo Zero, and AlphaZero series of algorithms are remarkable demonstrations of deep reinforcement learning's capabilities, achieving superhuman performance in the complex game of Go with progressively increasing autonomy.
Better Computer Go Player with Neural Network and Long-term Prediction
• facebookresearch/darkforestGo
• 
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.
Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model
• werner-duvaud/muzero-general
•
When evaluated on Go, chess and shogi, without any knowledge of the game rules, MuZero matched the superhuman performance of the AlphaZero algorithm that was supplied with the game rules.
Ranked #1 on
Atari Games
on Atari 2600 Zaxxon
Accelerating Self-Play Learning in Go
By introducing several improvements to the AlphaZero process and architecture, we greatly accelerate self-play learning in Go, achieving a 50x reduction in computation over comparable methods.
Move Evaluation in Go Using Deep Convolutional Neural Networks
• jmgilmer/GoCNN
• 
The game of Go is more challenging than other board games, due to the difficulty of constructing a position or move evaluation function.
Teaching Deep Convolutional Neural Networks to Play Go
• jmgilmer/GoCNN
•
Our final networks are able to achieve move prediction accuracies of 41. 1% and 44. 4% on two different Go datasets, surpassing previous state of the art on this task by significant margins.
Hyper-Parameter Sweep on AlphaZero General
Therefore, in this paper, we choose 12 parameters in AlphaZero and evaluate how these parameters contribute to training.
Derived metrics for the game of Go -- intrinsic network strength assessment and cheat-detection
This gives an intrinsic strength measurement for the neural network.
The Computational Limits of Deep Learning
Deep learning's recent history has been one of achievement: from triumphing over humans in the game of Go to world-leading performance in image recognition, voice recognition, translation, and other tasks.