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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/fellowship/deep-and-wide-bandit
공식 코드https://github.com/finn-no/recsys-slates-dataset
공식 코드Dynamic Slate Recommendation with Gated Recurrent Units and Thompson Sampling
https://github.com/georgedeath/aegis
공식 코드https://github.com/gfonseca92/bandits-lib
공식 코드Improving Portfolio Optimization Results with Bandit Networks
https://github.com/haoyangzheng1996/ts_ulmc
공식 코드Accelerating Approximate Thompson Sampling with Underdamped Langevin Monte Carlo
https://github.com/harsh-kumar9/bandit_simulation
공식 코드Using Adaptive Bandit Experiments to Increase and Investigate Engagement in Mental Health
https://github.com/hmishfaq/lmc-lsvi
공식 코드Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo
https://github.com/hmishfaq/lsac
공식 코드Langevin Soft Actor-Critic: Efficient Exploration through Uncertainty-Driven Critic Learning
https://github.com/hz0000/ebucb
공식 코드https://github.com/imadaouali/mixed-effect-thompson-sampling
공식 코드https://github.com/int-unl/atlas
공식 코드Atlas: Automate Online Service Configuration in Network Slicing
https://github.com/iosband/ts_tutorial
공식 코드https://github.com/itaishufaro/bitsandbandits
공식 코드On Bits and Bandits: Quantifying the Regret-Information Trade-off
https://github.com/iurteaga/bandits
공식 코드Bayesian bandits: balancing the exploration-exploitation tradeoff via double sampling
https://github.com/iurteaga/gp_ts_nlp
공식 코드https://github.com/jazzbob/stb
공식 코드https://github.com/jenna-fromer/qpo
공식 코드Batched Bayesian optimization by maximizing the probability of including the optimum
https://github.com/jkomiyama/multiplaybanditlib
공식 코드https://github.com/joel-ql-chang/continuous-rho-ts
공식 코드A Unifying Theory of Thompson Sampling for Continuous Risk-Averse Bandits
https://github.com/jsunn-y/SGPO
공식 코드Steering Generative Models with Experimental Data for Protein Fitness Optimization
https://github.com/junghunkim7786/dynamicassortmentselectionpricing
공식 코드Dynamic Assortment Selection and Pricing with Censored Preference Feedback
https://github.com/junghunkim7786/queueing-matching-bandits-with-preference-feedback
공식 코드https://github.com/kazizzad/BDQN-MxNet-Gluon
공식 코드https://github.com/licong-lin/in-context-rl
공식 코드https://github.com/likelyt/double-matching
공식 코드https://github.com/manome/python-mab
공식 코드Simple Modification of the Upper Confidence Bound Algorithm by Generalized Weighted Averages
https://github.com/maowenyu-11/TDM
공식 코드Addressing Missing Data Issue for Diffusion-based Recommendation
https://github.com/marcelbinz/resource-rational-reinforcement-learning
공식 코드Modeling Human Exploration Through Resource-Rational Reinforcement Learning
https://github.com/mskyt88/info-relax-sampling
공식 코드https://github.com/nikonikolov/rltf
공식 코드Information-Directed Exploration for Deep Reinforcement Learning
https://github.com/nirgreshler/bayesian-online-planning
공식 코드https://github.com/nphdang/bandit-bo
공식 코드Bayesian Optimization for Categorical and Category-Specific Continuous Inputs
https://github.com/numalariamodeling/covid-chicago
공식 코드Trajectory-oriented optimization of stochastic epidemiological models
https://github.com/nus-lid/sane
공식 코드State-Aware Variational Thompson Sampling for Deep Q-Networks
https://github.com/panxulab/eps-multi-agent-thompson-sampling
공식 코드Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.