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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1–25 of 62 papers

TitleStatusHype
Reinforcement Learning in Strategy-Based and Atari Games: A Review of Google DeepMinds Innovations—0
MASTER: A Multi-Agent System with LLM Specialized MCTS—0
Perceptual Similarity for Measuring Decision-Making Style and Policy Diversity in GamesCode0
Deep Reinforcement Learning for 5*5 Multiplayer Go—0
Monte Carlo Tree Search with Boltzmann ExplorationCode0
Task Success is not Enough: Investigating the Use of Video-Language Models as Behavior Critics for Catching Undesirable Agent Behaviors—0
Active Reinforcement Learning for Robust Building ControlCode1
Explaining How a Neural Network Play the Go Game and Let People Learn—0
Vision Transformers for Computer Go—0
AlphaZero Gomoku—0
Are AlphaZero-like Agents Robust to Adversarial Perturbations?Code1
The ProfessionAl Go annotation datasEt (PAGE)—0
The cost of passing -- using deep learning AIs to expand our understanding of the ancient game of Go—0
Score vs. Winrate in Score-Based Games: which Reward for Reinforcement Learning?—0
Spatial State-Action Features for General Games—0
Planning in Stochastic Environments with a Learned ModelCode1
Probabilistic DAG Search—0
Learning and Planning in Complex Action SpacesCode0
Batch Monte Carlo Tree Search—0
Conservative Optimistic Policy Optimization via Multiple Importance SamplingCode0
Visualizing MuZero ModelsCode1
Derived metrics for the game of Go -- intrinsic network strength assessment and cheat-detectionCode0
Mobile Networks for Computer Go—0
The Computational Limits of Deep LearningCode0
The Go Transformer: Natural Language Modeling for Game Play—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AlphaGo ZeroELO Rating5,185—Unverified