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Multi-Armed Bandits

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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/faaizt/mr-ope

공식 코드

Marginal Density Ratio for Off-Policy Evaluation in Contextual Bandits

https://github.com/fangkongx/tsformatchingmarkets

공식 코드

Thompson Sampling for Bandit Learning in Matching Markets

https://github.com/fellowship/deep-and-wide-bandit

공식 코드

Evaluating Deep Vs. Wide & Deep Learners As Contextual Bandits For Personalized Email Promo Recommendations

https://github.com/finn-no/recsys-slates-dataset

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Dynamic Slate Recommendation with Gated Recurrent Units and Thompson Sampling

https://github.com/fmzennaro/causally-abstracted-multiarmed-bandits

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Causally Abstracted Multi-armed Bandits

https://github.com/free-wei/dynamic_virtual_network_placement

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Online Learning for Function Placement in Serverless Computing

https://github.com/georgedeath/aegis

공식 코드

Asynchronous ε-Greedy Bayesian Optimisation

https://github.com/gerdm/BONE

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A unifying framework for generalised Bayesian online learning in non-stationary environments

https://github.com/gfonseca92/bandits-lib

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Improving Portfolio Optimization Results with Bandit Networks

https://github.com/gisoo1989/Doubly-Robust-Lasso-Bandit

공식 코드

Doubly-Robust Lasso Bandit

https://github.com/gjain234/whirl

공식 코드

IRL for Restless Multi-Armed Bandits with Applications in Maternal and Child Health

https://github.com/google-research/federated

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An Empirical Evaluation of Federated Contextual Bandit Algorithms

https://github.com/google-research/socialgood

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Indexability is Not Enough for Whittle: Improved, Near-Optimal Algorithms for Restless Bandits

https://github.com/govinda-kamath/combinatorial_MAB

공식 코드

Bandit-Based Monte Carlo Optimization for Nearest Neighbors

https://github.com/gsbDBI/PolicyLearning

공식 코드

Policy Learning with Adaptively Collected Data

https://github.com/gsbDBI/adaptive-confidence-intervals

공식 코드

Confidence Intervals for Policy Evaluation in Adaptive Experiments

https://github.com/gsbDBI/contextual_bandits_evaluation

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Off-Policy Evaluation via Adaptive Weighting with Data from Contextual Bandits

https://github.com/guoyihonggyh/distributionally-robust-policy-evaluation-under-general-covariate-shift-in-contextual-bandits

공식 코드

Distributionally Robust Policy Evaluation under General Covariate Shift in Contextual Bandits

https://github.com/haanvid/kmis

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Local Metric Learning for Off-Policy Evaluation in Contextual Bandits with Continuous Actions

https://github.com/haoyangzheng1996/ts_ulmc

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Accelerating Approximate Thompson Sampling with Underdamped Langevin Monte Carlo

https://github.com/haoyuzhao123/robust-cmab-code

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Adversarial Attacks on Combinatorial Multi-Armed Bandits

https://github.com/harsh-kumar9/bandit_simulation

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Using Adaptive Bandit Experiments to Increase and Investigate Engagement in Mental Health

https://github.com/heymarco/omegaucb

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Budgeted Multi-Armed Bandits with Asymmetric Confidence Intervals

https://github.com/hmishfaq/lmc-lsvi

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Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo

https://github.com/hmishfaq/lsac

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Langevin Soft Actor-Critic: Efficient Exploration through Uncertainty-Driven Critic Learning

https://github.com/holarissun/Data-Centric-OPE

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When is Off-Policy Evaluation (Reward Modeling) Useful in Contextual Bandits? A Data-Centric Perspective

https://github.com/huang-research-group/LDPbandit2020

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Locally Differentially Private (Contextual) Bandits Learning

https://github.com/huanghanchi/master-slave-algorithm-for-top-k-bandits

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Master-slave Deep Architecture for Top-K Multi-armed Bandits with Non-linear Bandit Feedback and Diversity Constraints

https://github.com/humainlab/human-bandit-evaluation

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A Field Test of Bandit Algorithms for Recommendations: Understanding the Validity of Assumptions on Human Preferences in Multi-armed Bandits

https://github.com/hz0000/ebucb

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Optimal Regret Is Achievable with Bounded Approximate Inference Error: An Enhanced Bayesian Upper Confidence Bound Framework

https://github.com/ilia-mahrooghi/multi-armed-bandits-with-missing-outcome

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Multi-armed Bandits with Missing Outcome

https://github.com/imadaouali/mixed-effect-thompson-sampling

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Mixed-Effect Thompson Sampling

https://github.com/imperial-qore/splitplace

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SplitPlace: AI Augmented Splitting and Placement of Large-Scale Neural Networks in Mobile Edge Environments

https://github.com/int-unl/atlas

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Atlas: Automate Online Service Configuration in Network Slicing

https://github.com/iosband/ts_tutorial

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A Tutorial on Thompson Sampling

https://github.com/itaishufaro/bitsandbandits

공식 코드

On Bits and Bandits: Quantifying the Regret-Information Trade-off

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