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Multi-Armed Bandits
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
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- 보존된 평가 기록
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Multi-armed bandits refer to a task where a fixed amount of resources must be allocated between competing resources that maximizes expected gain. Typically these problems involve an exploration/exploitation trade-off.
<span style="color:grey; opacity: 0.6">( Image credit: Microsoft Research )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/tensorflow/models/tree/archive/research/deep_contextual_bandits
https://github.com/timo-verstraeten/mats-experiments
Multi-Agent Thompson Sampling for Bandit Applications with Sparse Neighbourhood Structures
https://github.com/uclaml/neuralucb
https://github.com/wbwang2020/MP-MAB
Multiplayer Multi-armed Bandits for Optimal Assignment in Heterogeneous Networks
https://github.com/yanyangbaobeiIsEmma/Reinforcement-Learning-Contextual-Bandits
Thompson Sampling for Contextual Bandits with Linear Payoffs
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.