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 1–50 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
Tackling Morpion Solitaire with AlphaZero-likeRanked Reward Reinforcement Learning—0
Mastering Atari, Go, Chess and Shogi by Planning with a Learned ModelCode2
Multi-step Greedy Reinforcement Learning Algorithms—0
Multi-step Greedy Policies in Model-Free Deep Reinforcement Learning—0
MoET: Interpretable and Verifiable Reinforcement Learning via Mixture of Expert Trees—0
Playing Go without Game Tree Search Using Convolutional Neural Networks—0
Designing Game of Theorems—0
MoËT: Mixture of Expert Trees and its Application to Verifiable Reinforcement LearningCode1
Building a Computer Mahjong Player via Deep Convolutional Neural Networks—0
Generative Adversarial Imagination for Sample Efficient Deep Reinforcement Learning—0
Hyper-Parameter Sweep on AlphaZero GeneralCode1
Accelerating Self-Play Learning in GoCode2
ELF OpenGo: An Analysis and Open Reimplementation of AlphaZeroCode0
A GFML-based Robot Agent for Human and Machine Cooperative Learning on Game of Go—0
Comparing Knowledge-based Reinforcement Learning to Neural Networks in a Strategy Game—0
PFML-based Semantic BCI Agent for Game of Go Learning and Prediction—0
Meta-modeling game for deriving theoretical-consistent, micro-structural-based traction-separation laws via deep reinforcement learning—0
Pressure Predictions of Turbine Blades with Deep Learning—0
What do we need to build explainable AI systems for the medical domain?—0
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning AlgorithmCode1
FML-based Dynamic Assessment Agent for Human-Machine Cooperative System on Game of GoCode0
First-spike based visual categorization using reward-modulated STDP—0
A Popperian Falsification of Artificial Intelligence -- Lighthill Defended—0
Monte Carlo Tree Search with Sampled Information Relaxation Dual Bounds—0
FML-based Prediction Agent and Its Application to Game of Go—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AlphaGo ZeroELO Rating5,185—Unverified