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Board Games

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/AI4SC/archi-guesser

공식 코드

ArchiGuesser -- AI Art Architecture Educational Game

https://github.com/ArmanMaesumi/LimitedLookAheadChess

공식 코드

Playing Chess with Limited Look Ahead

https://github.com/DSobscure/cgi_drl_platform

공식 코드

Perceptual Similarity for Measuring Decision-Making Style and Policy Diversity in Games

https://github.com/GAIGResearch/TabletopGames

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Design and Implementation of TAG: A Tabletop Games Framework

https://github.com/NelsonAU/ale_countstates

공식 코드

Estimates for the Branching Factors of Atari Games

https://github.com/QueensGambit/CrazyAra

공식 코드

Learning to play the Chess Variant Crazyhouse above World Champion Level with Deep Neural Networks and Human Data

https://github.com/QueensGambit/CrazyAra-Engine

공식 코드

Learning to play the Chess Variant Crazyhouse above World Champion Level with Deep Neural Networks and Human Data

https://github.com/Unity-Technologies/obstacle-tower-env

공식 코드

Obstacle Tower: A Generalization Challenge in Vision, Control, and Planning

https://github.com/WolfLo/optimist

공식 코드

Conservative Optimistic Policy Optimization via Multiple Importance Sampling

https://github.com/abhi460729/Action-Duration-with-Contextual-Bandits-for-Deep-Reinforcement-Learning-in-Dynamic-Environments

공식 코드

Adaptive Action Duration with Contextual Bandits for Deep Reinforcement Learning in Dynamic Environments

https://github.com/anvo25/vlms-are-biased

공식 코드

Vision Language Models are Biased

https://github.com/cooper-org/cooper

공식 코드

Active Reinforcement Learning for Robust Building Control

https://github.com/datamllab/rlcard

공식 코드

RLCard: A Toolkit for Reinforcement Learning in Card Games

https://github.com/deepmind/open_spiel

공식 코드

Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning

https://github.com/demosthen/activerl

공식 코드

Active Reinforcement Learning for Robust Building Control

https://github.com/divyakoyy/codenames

공식 코드

Playing Codenames with Language Graphs and Word Embeddings

https://github.com/egri-nagy/lambdago

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Derived metrics for the game of Go -- intrinsic network strength assessment and cheat-detection

https://github.com/facebookincubator/Polygames

공식 코드

Deep Learning for General Game Playing with Ludii and Polygames

https://github.com/facebookresearch/darkforestGo

공식 코드

Better Computer Go Player with Neural Network and Long-term Prediction

https://github.com/gdrtodd/gavel

공식 코드

GAVEL: Generating Games Via Evolution and Language Models

https://github.com/guilherme439/nuzero

공식 코드

Playing Hex and Counter Wargames using Reinforcement Learning and Recurrent Neural Networks

https://github.com/hannasw/ml4passportoptions

공식 코드

Machine Learning-powered Pricing of the Multidimensional Passport Option

https://github.com/jzf2101/alphatology

공식 코드

Evaluation Beyond Task Performance: Analyzing Concepts in AlphaZero in Hex

https://github.com/kaesve/muzero

공식 코드

Visualizing MuZero Models

https://github.com/kandluis/terrazero

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Mastering Terra Mystica: Applying Self-Play to Multi-agent Cooperative Board Games

https://github.com/kazuhisafujita/alphadda

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AlphaDDA: Strategies for Adjusting the Playing Strength of a Fully Trained AlphaZero System to a Suitable Human Training Partner

https://github.com/kazuhisafujita/alphavit

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AlphaViT: A Flexible Game-Playing AI for Multiple Games and Variable Board Sizes

https://github.com/konsfik/Pandemic-AI-Framework

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Playing Against the Board: Rolling Horizon Evolutionary Algorithms Against Pandemic

https://github.com/lan-lc/adversarial_example_of_Go

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Are AlphaZero-like Agents Robust to Adversarial Perturbations?

https://github.com/lightvector/KataGo

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Accelerating Self-Play Learning in Go

https://github.com/malhotra-sidharth/game-of-ur-reinforcement-learning

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Solving Royal Game of Ur Using Reinforcement Learning

https://github.com/marcoantonioaav/hybrid-minimax-mcts

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Hybrid Minimax-MCTS and Difficulty Adjustment for General Game Playing

https://github.com/marekesz/rbg

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Split Moves for Monte-Carlo Tree Search

https://github.com/marekesz/rbg1.0

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Regular Boardgames

https://github.com/marko-vasic/moet

공식 코드

MoËT: Mixture of Expert Trees and its Application to Verifiable Reinforcement Learning

https://github.com/martinballa/pytag

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PyTAG: Challenges and Opportunities for Reinforcement Learning in Tabletop Games

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