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
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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.
Stay With Me: Lifetime Maximization Through Heteroscedastic Linear Bandits With Reneging
• Xi-Liu/heteroscedasticbandits
Sequential decision making for lifetime maximization is a critical problem in many real-world applications, such as medical treatment and portfolio selection.
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.