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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/PlaytikaOSS/pybandits

Thompson Sampling for Contextual Bandits with Linear Payoffs

https://github.com/Ralami1859/Stochastic-Multi-Armed-Bandit

Thompson Sampling: An Asymptotically Optimal Finite Time Analysis

https://github.com/RonyAbecidan/Neural-Thompson-Sampling

Neural Thompson Sampling

https://github.com/anushalalitha5/Decentralized-Thompson-Sampling

Bayesian Algorithms for Decentralized Stochastic Bandits

https://github.com/baekjin-kim/UAI2020_NonstationaryLinearBandit

Randomized Exploration for Non-Stationary Stochastic Linear Bandits

https://github.com/bettyttytty/Thompson-Sampling-for-a-Fatigue-aware-Online-Recommendation-System

Thompson Sampling for a Fatigue-aware Online Recommendation System

https://github.com/ctrnh/LinearContextualBandits

Mostly Exploration-Free Algorithms for Contextual Bandits

https://github.com/dsweet99/yubo

Fast, Precise Thompson Sampling for Bayesian Optimization

https://github.com/duanyiyao/E-TS-in-VFL

Constructing Adversarial Examples for Vertical Federated Learning: Optimal Client Corruption through Multi-Armed Bandit

https://github.com/facebookresearch/RandomizedValueFunctions

Randomized Value Functions via Multiplicative Normalizing Flows

https://github.com/feji3769/VecchiaBO

Scalable Bayesian Optimization Using Vecchia Approximations of Gaussian Processes

https://github.com/finn-no/recsys_slates_dataset

Dynamic Slate Recommendation with Gated Recurrent Units and Thompson Sampling

https://github.com/kirthevasank/gp-parallel-ts

Asynchronous Parallel Bayesian Optimisation via Thompson Sampling

https://github.com/kirthevasank/mps

Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic Programming

https://github.com/ksetdekov/trip_choice_optimizer

Thompson Sampling Algorithms for Mean-Variance Bandits

https://github.com/maxqyzhu/TS_for_mean_variance_bandit

Thompson Sampling Algorithms for Mean-Variance Bandits

https://github.com/mlisicki/neuralkernelbandits

Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling

https://github.com/niravnb/Movie-Recommendation-using-Cascading-Bandits

Cascading Bandits for Large-Scale Recommendation Problems

https://github.com/oh-lab/UTE-Uncertainty-aware-Temporal-Extension-

Randomized Value Functions via Multiplicative Normalizing Flows

https://github.com/pwc-1/Paper-9/tree/main/5/vits

VITS : Variational Inference Thompson Sampling for contextual bandits

https://github.com/renan-cunha/Bandits

Adapting multi-armed bandits policies to contextual bandits scenarios

https://github.com/roysaptaumich/HD-bandit-codes

Thompson Sampling for High-Dimensional Sparse Linear Contextual Bandits

https://github.com/tensorflow/models

Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling

https://github.com/tensorflow/models/tree/archive/research/deep_contextual_bandits

Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling

https://github.com/timo-verstraeten/mats-experiments

Multi-Agent Thompson Sampling for Bandit Applications with Sparse Neighbourhood Structures

https://github.com/vectorinstitute/neuralkernelbandits

Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling

https://github.com/yanyangbaobeiIsEmma/Reinforcement-Learning-Contextual-Bandits

Thompson Sampling for Contextual Bandits with Linear Payoffs

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