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
Mastering Atari, Go, Chess and Shogi by Planning with a Learned ModelCode2
Accelerating Self-Play Learning in GoCode2
Are AlphaZero-like Agents Robust to Adversarial Perturbations?Code1
Move Evaluation in Go Using Deep Convolutional Neural NetworksCode1
Hyper-Parameter Sweep on AlphaZero GeneralCode1
Planning in Stochastic Environments with a Learned ModelCode1
MoËT: Mixture of Expert Trees and its Application to Verifiable Reinforcement LearningCode1
Visualizing MuZero ModelsCode1
Teaching Deep Convolutional Neural Networks to Play GoCode1
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning AlgorithmCode1
Active Reinforcement Learning for Robust Building ControlCode1
Monte Carlo Tree Search with Boltzmann ExplorationCode0
FML-based Dynamic Assessment Agent for Human-Machine Cooperative System on Game of GoCode0
Derived metrics for the game of Go -- intrinsic network strength assessment and cheat-detectionCode0
Better Computer Go Player with Neural Network and Long-term PredictionCode0
Perceptual Similarity for Measuring Decision-Making Style and Policy Diversity in GamesCode0
The Computational Limits of Deep LearningCode0
ELF OpenGo: An Analysis and Open Reimplementation of AlphaZeroCode0
Learning and Planning in Complex Action SpacesCode0
Conservative Optimistic Policy Optimization via Multiple Importance SamplingCode0
Meta-modeling game for deriving theoretical-consistent, micro-structural-based traction-separation laws via deep reinforcement learning—0
Micro-Objective Learning : Accelerating Deep Reinforcement Learning through the Discovery of Continuous Subgoals—0
Mobile Networks for Computer Go—0
MoET: Interpretable and Verifiable Reinforcement Learning via Mixture of Expert Trees—0
Monte Carlo Tree Search with Sampled Information Relaxation Dual Bounds—0
Multi-step Greedy Policies in Model-Free Deep Reinforcement Learning—0
Multi-step Greedy Reinforcement Learning Algorithms—0
Neurohex: A Deep Q-learning Hex Agent—0
PFML-based Semantic BCI Agent for Game of Go Learning and Prediction—0
Playing Go without Game Tree Search Using Convolutional Neural Networks—0
Pressure Predictions of Turbine Blades with Deep Learning—0
Probabilistic DAG Search—0
Reinforcement Learning in Strategy-Based and Atari Games: A Review of Google DeepMinds Innovations—0
Selecting Computations: Theory and Applications—0
Spatial State-Action Features for General Games—0
Tackling Morpion Solitaire with AlphaZero-likeRanked Reward Reinforcement Learning—0
Task Success is not Enough: Investigating the Use of Video-Language Models as Behavior Critics for Catching Undesirable Agent Behaviors—0
The cost of passing -- using deep learning AIs to expand our understanding of the ancient game of Go—0
The Go Transformer: Natural Language Modeling for Game Play—0
The ProfessionAl Go annotation datasEt (PAGE)—0
Towards Deep Symbolic Reinforcement Learning—0
Vision Transformers for Computer Go—0
What do we need to build explainable AI systems for the medical domain?—0
A GFML-based Robot Agent for Human and Machine Cooperative Learning on Game of Go—0
AlphaZero Gomoku—0
A Popperian Falsification of Artificial Intelligence -- Lighthill Defended—0
Bandit Algorithms for Tree Search—0
Batch Monte Carlo Tree Search—0
Building a Computer Mahjong Player via Deep Convolutional Neural Networks—0
Can Machine Generate Traditional Chinese Poetry? A Feigenbaum Test—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AlphaGo ZeroELO Rating5,185—Unverified