Python Bandit Resources

Code for the paper Jointly Efficient and Optimal Algorithms for Logistic Bandits, by Louis Faury, Marc Abeille, Clément Calauzènes and Kwang-Sun Jun.

batch-bandits Implementation of popular bandit algorithms in batch environments. Source code to our paper "The Impact of Batch Learning in Stochastic

Contents LDP LinUCB Description Model Architecture Dataset Environment Requirements Script Description Script and Sample Code Script Parameters Launch

This is code repo for our EMNLP 2017 paper "Reinforcement Learning for Bandit Neural Machine Translation with Simulated Human Feedback", which implements the A2C algorithm on top of a neural encoder-decoder model and benchmarks the combination under simulated noisy rewards.

banditml is a lightweight contextual bandit & reinforcement learning library designed to be used in production Python services. This library is developed by Bandit ML and ex-authors of Facebook's applied reinforcement learning platform, Reagent.

iter8-analytics iter8 enables statistically robust continuous experimentation of microservices in your CI/CD pipelines. For in-depth information about

A security linter from PyCQA Free software: Apache license Documentation: https://bandit.readthedocs.io/en/latest/ Source: https://github.com/PyCQA/ba

flake8-bandit Automated security testing built right into your workflow! You already use flake8 to lint all your code for errors, ensure docstrings ar