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Multi-Armed Bandits

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보존된 평가 기록: 2

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://bitbucket.org/scee_ietr/malin-multi-armed-bandit-learning-for-iot-networks-with-grc

공식 코드

Reinforcement Learning for Physical Layer Communications

https://github.com/5tefan0/Regulating-Greed-Over-Time

공식 코드

Regulating Greed Over Time in Multi-Armed Bandits

https://github.com/66661654/Raven-UCB

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From Theory to Practice with RAVEN-UCB: Addressing Non-Stationarity in Multi-Armed Bandits through Variance Adaptation

https://github.com/AaltoPML/machine-teaching-of-active-sequential-learners

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Machine Teaching of Active Sequential Learners

https://github.com/Annie983284450-1/NeuralLCB_C

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NeuroSep-CP-LCB: A Deep Learning-based Contextual Multi-armed Bandit Algorithm with Uncertainty Quantification for Early Sepsis Prediction

https://github.com/BorealisAI/jump-starting-bandits

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Jump Starting Bandits with LLM-Generated Prior Knowledge

https://github.com/DBaudry/Sub-Sampling-Dueling-Algorithms-Neurips20

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Sub-sampling for Efficient Non-Parametric Bandit Exploration

https://github.com/FMZennaro/SLBandits

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Using Subjective Logic to Estimate Uncertainty in Multi-Armed Bandit Problems

https://github.com/HarmoniaLeo/PRZI-Bayesian-Optimisation

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Nonstationary Continuum-Armed Bandit Strategies for Automated Trading in a Simulated Financial Market

https://github.com/HuasenWu/DuelingBandits

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Double Thompson Sampling for Dueling Bandits

https://github.com/IBM/sau-explore

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Deep Bandits Show-Off: Simple and Efficient Exploration with Deep Networks

https://github.com/JimyZ13/ADDRESS

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Anytime Multi-Agent Path Finding with an Adaptive Delay-Based Heuristic

https://github.com/Kapilan-Balagopalan/Linear-Bandit-Algorithms

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Minimum Empirical Divergence for Sub-Gaussian Linear Bandits

https://github.com/Karlmyh/LDP-Contextual-MAB

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Locally Private Nonparametric Contextual Multi-armed Bandits

https://github.com/Marksmug/CEVAE-Transportability

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Transfer Learning in Latent Contextual Bandits with Covariate Shift Through Causal Transportability

https://github.com/Markusmu93/J-CompChE-HPCBoTS

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Process-constrained batch Bayesian approaches for yield optimization in multi-reactor systems

https://github.com/Mathegineer/batched-bandit

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Batched Multi-armed Bandits Problem

https://github.com/Mengyanz/QSAR

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Quantile Bandits for Best Arms Identification

https://github.com/MjolnirT/Kullback-Leibler-Maillard-Sampling

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Kullback-Leibler Maillard Sampling for Multi-armed Bandits with Bounded Rewards

https://github.com/Piyushi-0/Fair-MAMAB

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Multi-agent Multi-armed Bandits with Minimum Reward Guarantee Fairness

https://github.com/PredictiveIntelligenceLab/jax-bandits

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Output-Weighted Sampling for Multi-Armed Bandits with Extreme Payoffs

https://github.com/Quoding/optimneuralbandits

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Neural Bandits for Data Mining: Searching for Dangerous Polypharmacy

https://github.com/RaymZhang/TS_Combinatorial_Semi_Bandits_Curse

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On the Suboptimality of Thompson Sampling in High Dimensions

https://github.com/ShacharSchnapp/ActiveFeatureSelection

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Active Feature Selection for the Mutual Information Criterion

https://github.com/ShenGroup/FMAB

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Federated Multi-Armed Bandits

https://github.com/ShenGroup/PF_MAB

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Federated Multi-armed Bandits with Personalization

https://github.com/UM-iRaL/Self-configurable-network

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Performance-Aware Self-Configurable Multi-Agent Networks: A Distributed Submodular Approach for Simultaneous Coordination and Network Design

https://github.com/VowpalWabbit/slope-experiments

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Adaptive Estimator Selection for Off-Policy Evaluation

https://github.com/YannDubs/Neural-Process-Family

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Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes

https://github.com/ZeroWeight/NeuralTS

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Neural Thompson Sampling

https://github.com/Zhendong-Wang/Thompson-Sampling-via-Local-Uncertainty

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Thompson Sampling via Local Uncertainty

https://github.com/aagarwal1996/NetworkMAB

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Multi-Armed Bandits with Network Interference

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

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

https://github.com/adaptivecontextualcausalbandits/accb

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Causal Contextual Bandits with Adaptive Context

https://github.com/adhyyannarang/online_bp

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Online SuBmodular + SuPermodular (BP) Maximization with Bandit Feedback

https://github.com/aistats2024-noisy-psne/midsearch

공식 코드

Near-Optimal Pure Exploration in Matrix Games: A Generalization of Stochastic Bandits & Dueling Bandits

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