Card Games
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 보존된 평가 기록
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Card games involve playing cards: the task is to train an agent to play the game with specified rules and beat other players.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/deepmind/open_spiel
Deep Reinforcement Learning from Self-Play in Imperfect-Information Games
https://github.com/facebookresearch/Hanabi_SAD
Improving Policies via Search in Cooperative Partially Observable Games
https://github.com/facebookresearch/hanabi
Improving Policies via Search in Cooperative Partially Observable Games
https://github.com/facebookresearch/off-belief-learning
Improving Policies via Search in Cooperative Partially Observable Games
https://github.com/gfppoy/hanabi-belief-transformer
Improving Policies via Search in Cooperative Partially Observable Games
https://github.com/gfppoy/hanabi-br-withbelief
Improving Policies via Search in Cooperative Partially Observable Games
https://github.com/gfppoy/hanabi-br-wobelief
Improving Policies via Search in Cooperative Partially Observable Games
https://github.com/heidekrueger/bnelearn
Deep Reinforcement Learning from Self-Play in Imperfect-Information Games
https://github.com/jasonisgod/Big2
Application of Self-Play Reinforcement Learning to a Four-Player Game of Imperfect Information
https://github.com/jsanderink/tue
Deep Reinforcement Learning from Self-Play in Imperfect-Information Games
https://github.com/leonguertler/textarena
GTBench: Uncovering the Strategic Reasoning Limitations of LLMs via Game-Theoretic Evaluations
https://github.com/lifrordi/DeepStack-Leduc
DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker
https://github.com/mjiang9/_rlcard
https://github.com/mjiang9/rlcard-copy
https://github.com/q275212/rlcard
https://github.com/quantumiracle/mars
Deep Reinforcement Learning from Self-Play in Imperfect-Information Games
https://github.com/tobiasemrich/SchafkopfRL
Survey of Artificial Intelligence for Card Games and Its Application to the Swiss Game Jass
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.