Multi-Armed Bandits
227 papers with code • 1 benchmarks • 2 datasets
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.
( Image credit: Microsoft Research )
Benchmarks
These leaderboards are used to track progress in Multi-Armed Bandits
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Libraries
Use these libraries to find Multi-Armed Bandits models and implementations
KuaiRand
Duolingo Bandit Notifications
Most implemented papers
Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions Modeling
sb-ai-lab/RePlay
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The DRR framework treats recommendation as a sequential decision making procedure and adopts an "Actor-Critic" reinforcement learning scheme to model the interactions between the users and recommender systems, which can consider both the dynamic adaptation and long-term rewards.
Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling
tensorflow/models
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ICLR 2018
At the same time, advances in approximate Bayesian methods have made posterior approximation for flexible neural network models practical.
Neural Contextual Bandits with UCB-based Exploration
sauxpa/neural_exploration
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ICML 2020
To the best of our knowledge, it is the first neural network-based contextual bandit algorithm with a near-optimal regret guarantee.
On-line Adaptative Curriculum Learning for GANs
We argue that less expressive discriminators are smoother and have a general coarse grained view of the modes map, which enforces the generator to cover a wide portion of the data distribution support.
Locally Differentially Private (Contextual) Bandits Learning
huang-research-group/LDPbandit2020
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NeurIPS 2020
We study locally differentially private (LDP) bandits learning in this paper.
Gaussian Gated Linear Networks
deepmind/deepmind-research
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NeurIPS 2020
We propose the Gaussian Gated Linear Network (G-GLN), an extension to the recently proposed GLN family of deep neural networks.
Online Limited Memory Neural-Linear Bandits with Likelihood Matching
mlisicki/neuralkernelbandits
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To alleviate this, we propose a likelihood matching algorithm that is resilient to catastrophic forgetting and is completely online.
Off-Policy Evaluation for Large Action Spaces via Embeddings
Unfortunately, when the number of actions is large, existing OPE estimators -- most of which are based on inverse propensity score weighting -- degrade severely and can suffer from extreme bias and variance.
Multi-Armed Bandits in Metric Spaces
In this work we study a very general setting for the multi-armed bandit problem in which the strategies form a metric space, and the payoff function satisfies a Lipschitz condition with respect to the metric.
Thompson Sampling for Contextual Bandits with Linear Payoffs
Thompson Sampling is one of the oldest heuristics for multi-armed bandit problems.
