Multi-Armed Bandits
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.
State-of-the-art leaderboards
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Greatest papers with code
Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling
ICLR 2018
• tensorflow/models
• 
At the same time, advances in approximate Bayesian methods have made posterior approximation for flexible neural network models practical.
SOTA for Multi-Armed Bandits on Mushroom
Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits
We present a new algorithm for the contextual bandit learning problem, where the learner repeatedly takes one of $K$ actions in response to the observed context, and observes the reward only for that chosen action.
Adapting multi-armed bandits policies to contextual bandits scenarios
• david-cortes/contextualbandits
This work explores adaptations of successful multi-armed bandits policies to the online contextual bandits scenario with binary rewards using binary classification algorithms such as logistic regression as black-box oracles.
Learning Structural Weight Uncertainty for Sequential Decision-Making
Learning probability distributions on the weights of neural networks (NNs) has recently proven beneficial in many applications.
A Survey on Contextual Multi-armed Bandits
• yanyangbaobeiIsEmma/Reinforcement-Learning-Contextual-Bandits
In this survey we cover a few stochastic and adversarial contextual bandit algorithms.
Thompson Sampling for Contextual Bandits with Linear Payoffs
• yanyangbaobeiIsEmma/Reinforcement-Learning-Contextual-Bandits
Thompson Sampling is one of the oldest heuristics for multi-armed bandit problems.
The Assistive Multi-Armed Bandit
Learning preferences implicit in the choices humans make is a well studied problem in both economics and computer science.
On-line Adaptative Curriculum Learning for GANs
• Byte7/Adaptive-Curriculum-GAN-keras
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.
Heteroscedastic Bandits with Reneging
• Xi-Liu/heteroscedasticbandits
Although shown to be useful in many areas as models for solving sequential decision problems with side observations (contexts), contextual bandits are subject to two major limitations.
Contextual Bandits with Stochastic Experts
• rajatsen91/CB_StochasticExperts
We consider the problem of contextual bandits with stochastic experts, which is a variation of the traditional stochastic contextual bandit with experts problem.