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Board Games

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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코드

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

https://github.com/plkmo/AlphaZero_Connect4

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

https://github.com/q275212/rlcard

RLCard: A Toolkit for Reinforcement Learning in Card Games

https://github.com/qinwang/leela-zero

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

https://github.com/ray-project/ray/tree/master/rllib

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

https://github.com/rsdmse/KataGo

Accelerating Self-Play Learning in Go

https://github.com/ryanp73/ChessAI

Giraffe: Using Deep Reinforcement Learning to Play Chess

https://github.com/sagpant/leela-zero

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

https://github.com/sai-dev/sai

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

https://github.com/saikrishna-1996/deep_pepper_chess

Giraffe: Using Deep Reinforcement Learning to Play Chess

https://github.com/samhippie/q-game-agent

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

https://github.com/samiamkhan/4641Project

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

https://github.com/shaneallcroft/SSB64RLBot

Beating the World's Best at Super Smash Bros. with Deep Reinforcement Learning

https://github.com/shrumo/rbgcpp2py

Regular Boardgames

https://github.com/simoll/apo

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

https://github.com/simsal0r/mixture-of-decision-trees

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

https://github.com/snjstudent/MyMuzero

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

https://github.com/souvikshanku/HexapawnZero

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

https://github.com/suragnair/alpha-zero-general

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

https://github.com/svikramank/chess-deepRL

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

https://github.com/tensorflow/minigo

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

https://github.com/tianhai123/darkforestGo

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

https://github.com/tmoer/a0c

A0C: Alpha Zero in Continuous Action Space

https://github.com/ucdchessai/chess-ai

DeepChess: End-to-End Deep Neural Network for Automatic Learning in Chess

https://github.com/wangml999/chess_zero

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

https://github.com/wcarvalho/jaxneurorl

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

https://github.com/werner-duvaud/muzero-general

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

https://github.com/zhoujianxing123/minigo_v17

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.