https://github.com/michaelnny/alpha_zero
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
Papers with Code 아카이브
Game of Go
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/opendilab/LightZero
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/pathway/alphaxos
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/plkmo/AlphaZero_Connect4
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
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
https://github.com/ryanp73/ChessAI
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
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
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
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/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/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.