Skip to content

A benchmark to test decision-making algorithms for contextual-bandits. The library implements a variety of algorithms (many of them based on approximate Bayesian Neural Networks and Thompson sampling), and a number of real and syntethic data problems exhibiting a diverse set of properties.

Notifications You must be signed in to change notification settings

babaniyi/Deep-contextual-bandits

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

11 Commits
 
 
 
 
 
 

Repository files navigation

Deep Contextual Bandits

This library corresponds to the Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling paper, published in ICLR 2018. The authors provide a benchmark to test decision-making algorithms for contextual-bandits. In particular, the current library implements a variety of algorithms (many of them based on approximate Bayesian Neural Networks and Thompson sampling), and a number of real and syntethic data problems exhibiting a diverse set of properties.

It is a Python library that uses TensorFlow. The TensorFlow archive directory of the paper is located here.

Goal

The codes are not maintained anymore and I intend to create a package that allows it to work on any dataset.

About

A benchmark to test decision-making algorithms for contextual-bandits. The library implements a variety of algorithms (many of them based on approximate Bayesian Neural Networks and Thompson sampling), and a number of real and syntethic data problems exhibiting a diverse set of properties.

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages