SOTAVerified

Game of Go

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.

Papers

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Showing 1–25 of 62 papers

TitleStatusHype
Accelerating Self-Play Learning in GoCode2
Mastering Atari, Go, Chess and Shogi by Planning with a Learned ModelCode2
Hyper-Parameter Sweep on AlphaZero GeneralCode1
Visualizing MuZero ModelsCode1
MoËT: Mixture of Expert Trees and its Application to Verifiable Reinforcement LearningCode1
Active Reinforcement Learning for Robust Building ControlCode1
Planning in Stochastic Environments with a Learned ModelCode1
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning AlgorithmCode1
Are AlphaZero-like Agents Robust to Adversarial Perturbations?Code1
Teaching Deep Convolutional Neural Networks to Play GoCode1
Move Evaluation in Go Using Deep Convolutional Neural NetworksCode1
Deep Network Guided Proof Search—0
Comparing Knowledge-based Reinforcement Learning to Neural Networks in a Strategy Game—0
Deep Reinforcement Learning for 5*5 Multiplayer Go—0
Human vs. Computer Go: Review and Prospect—0
Generative Adversarial Imagination for Sample Efficient Deep Reinforcement Learning—0
Bandit Algorithms for Tree Search—0
Can Machine Generate Traditional Chinese Poetry? A Feigenbaum Test—0
Building a Computer Mahjong Player via Deep Convolutional Neural Networks—0
First-spike based visual categorization using reward-modulated STDP—0
FML-based Prediction Agent and Its Application to Game of Go—0
Functions that Emerge through End-to-End Reinforcement Learning - The Direction for Artificial General Intelligence -—0
A Popperian Falsification of Artificial Intelligence -- Lighthill Defended—0
Score vs. Winrate in Score-Based Games: which Reward for Reinforcement Learning?—0
Explaining How a Neural Network Play the Go Game and Let People Learn—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AlphaGo ZeroELO Rating5,185—Unverified