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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://github.com/manome/python-mab

공식 코드

Simple Modification of the Upper Confidence Bound Algorithm by Generalized Weighted Averages

https://github.com/maowenyu-11/TDM

공식 코드

Addressing Missing Data Issue for Diffusion-based Recommendation

https://github.com/marcelbinz/resource-rational-reinforcement-learning

공식 코드

Modeling Human Exploration Through Resource-Rational Reinforcement Learning

https://github.com/masoudmansoury/ealinucb

공식 코드

Mitigating Exposure Bias in Online Learning to Rank Recommendation: A Novel Reward Model for Cascading Bandits

https://github.com/mlisicki/neuralkernelbandits

공식 코드

Empirical analysis of representation learning and exploration in neural kernel bandits

https://github.com/motiwari/BanditPAM-python

공식 코드

BanditPAM: Almost Linear Time $k$-Medoids Clustering via Multi-Armed Bandits

https://github.com/mrucker/onoff_experiments

공식 코드

Infinite Action Contextual Bandits with Reusable Data Exhaust

https://github.com/mskyt88/info-relax-sampling

공식 코드

Thompson Sampling with Information Relaxation Penalties

https://github.com/mufangying/alee

공식 코드

Adaptive Linear Estimating Equations

https://github.com/multifair-bandits/stochastic_fair_bandits

공식 코드

Simultaneously Achieving Group Exposure Fairness and Within-Group Meritocracy in Stochastic Bandits

https://github.com/nar-k/persistent-excitation

공식 코드

Persistency of Excitation for Robustness of Neural Networks

https://github.com/nick-jhlee/bandits-network

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Flooding with Absorption: An Efficient Protocol for Heterogeneous Bandits over Complex Networks

https://github.com/nicomeyer96/quantum-natural-policy-gradients

공식 코드

Quantum Natural Policy Gradients: Towards Sample-Efficient Reinforcement Learning

https://github.com/nikonikolov/rltf

공식 코드

Information-Directed Exploration for Deep Reinforcement Learning

https://github.com/nirgreshler/bayesian-online-planning

공식 코드

A Bayesian Approach to Online Planning

https://github.com/nirjhar-das/glbandit_limited_adaptivity

공식 코드

Generalized Linear Bandits with Limited Adaptivity

https://github.com/nirjhar-das/hypay_bandits

공식 코드

Linear Contextual Bandits with Hybrid Payoff: Revisited

https://github.com/noesyslab/drl_ris_tutorial

공식 코드

Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfaces

https://github.com/nphdang/bandit-bo

공식 코드

Bayesian Optimization for Categorical and Category-Specific Continuous Inputs

https://github.com/nrgrp/cvx_imab

공식 코드

Solving Inverse Problem for Multi-armed Bandits via Convex Optimization

https://github.com/numalariamodeling/covid-chicago

공식 코드

Trajectory-oriented optimization of stochastic epidemiological models

https://github.com/nus-lid/sane

공식 코드

State-Aware Variational Thompson Sampling for Deep Q-Networks

https://github.com/panxulab/eps-multi-agent-thompson-sampling

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Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs

https://github.com/panxulab/lsvi-ase

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More Efficient Randomized Exploration for Reinforcement Learning via Approximate Sampling

https://github.com/panxulab/optimal-batched-linear-bandits

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Optimal Batched Linear Bandits

https://github.com/parththaker/Bandits-GRUB

공식 코드

Maximizing and Satisficing in Multi-armed Bandits with Graph Information

https://github.com/paulorauber/rnlps

공식 코드

Recurrent Neural-Linear Posterior Sampling for Nonstationary Contextual Bandits

https://github.com/pkassraie/NNUCB

공식 코드

Neural Contextual Bandits without Regret

https://github.com/pmineiro/elfcb

공식 코드

Empirical Likelihood for Contextual Bandits

https://github.com/pmineiro/linrepcb

공식 코드

Contextual Bandits with Large Action Spaces: Made Practical

https://github.com/pmineiro/smoothcb

공식 코드

Contextual Bandits with Smooth Regret: Efficient Learning in Continuous Action Spaces

https://github.com/posttraining/post_training

공식 코드

Human in the Loop Adaptive Optimization for Improved Time Series Forecasting

https://github.com/quan-zhou/mab

공식 코드

Representative Action Selection for Large Action-Space Meta-Bandits

https://github.com/quejialin/squarecbwk

공식 코드

Optimal Contextual Bandits with Knapsacks under Realizability via Regression Oracles

https://github.com/rahbararman/cost-efficient-odm

공식 코드

Cost-Efficient Online Decision Making: A Combinatorial Multi-Armed Bandit Approach

https://github.com/raihan-seraj/tsetlin-machine-for-solving-contextual-bandit-problems

공식 코드

Tsetlin Machine for Solving Contextual Bandit Problems

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