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Thompson Sampling

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Thompson sampling, named after William R. Thompson, is a heuristic for choosing actions that addresses the exploration-exploitation dilemma in the multi-armed bandit problem. It consists of choosing the action that maximizes the expected reward with respect to a randomly drawn belief.

코드

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

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

공식 코드

Sub-sampling for Efficient Non-Parametric Bandit Exploration

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/Markusmu93/J-CompChE-HPCBoTS

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

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/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/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/alexandertornede/online_as

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Machine Learning for Online Algorithm Selection under Censored Feedback

https://github.com/alimama-creative/AES-Adaptive-and-Efficient-ad-creative-Selection

공식 코드

Efficient Optimal Selection for Composited Advertising Creatives with Tree Structure

https://github.com/alimama-creative/AutoCO

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Automated Creative Optimization for E-Commerce Advertising

https://github.com/anndvision/vsop

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ReLU to the Rescue: Improve Your On-Policy Actor-Critic with Positive Advantages

https://github.com/aryandeshwal/MerCBO

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Mercer Features for Efficient Combinatorial Bayesian Optimization

https://github.com/aryandeshwal/bops

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Bayesian Optimization over Permutation Spaces

https://github.com/aslanides/aixijs

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AIXIjs: A Software Demo for General Reinforcement Learning

https://github.com/baekjin-kim/NonstationaryLB

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Randomized Exploration for Non-Stationary Stochastic Linear Bandits

https://github.com/banyikun/ee-net-iclr-2022

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EE-Net: Exploitation-Exploration Neural Networks in Contextual Bandits

https://github.com/bilkent-cyborg/escada

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ESCADA: Efficient Safety and Context Aware Dose Allocation for Precision Medicine

https://github.com/bvarici/causal-bandits-linear-sem

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Causal Bandits for Linear Structural Equation Models

https://github.com/chang-li/MergeDTS

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MergeDTS: A Method for Effective Large-Scale Online Ranker Evaluation

https://github.com/chaoukia/thompson-sampling-decision-trees

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Online Learning of Decision Trees with Thompson Sampling

https://github.com/csdlpsu/qpots

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qPOTS: Efficient batch multiobjective Bayesian optimization via Pareto optimal Thompson sampling

https://github.com/daizhongxiang/Federated_Bayesian_Optimization

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Federated Bayesian Optimization via Thompson Sampling

https://github.com/daizhongxiang/meta-bo

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On Provably Robust Meta-Bayesian Optimization

https://github.com/daizhongxiang/sto-bnts

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Sample-Then-Optimize Batch Neural Thompson Sampling

https://github.com/david-cortes/contextualbandits

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Adapting multi-armed bandits policies to contextual bandits scenarios

https://github.com/davidrey123/vaccine_allocation

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Vaccine allocation policy optimization and budget sharing mechanism using Thompson sampling

https://github.com/deepmind/enn_acme

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Approximate Thompson Sampling via Epistemic Neural Networks

https://github.com/devzhk/lmcts

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Langevin Monte Carlo for Contextual Bandits

https://github.com/dimarkov/aibandits

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An empirical evaluation of active inference in multi-armed bandits

https://github.com/doerlbh/BanditZoo

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Evolutionary Multi-Armed Bandits with Genetic Thompson Sampling

https://github.com/eastonhuch/rome

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RoME: A Robust Mixed-Effects Bandit Algorithm for Optimizing Mobile Health Interventions

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