Our work is based on an article from Russo & Van Roy (2018): Learning to optimize via Information Directed Sampling . The aim of this project is to propose a platform that allows to reproduce the experiments of the paper and to easily implement new examples. We first implement classes to implement a general Multi-Arm Bandit setting. Then, we provide some problem specific classes that implements different versions of IDS and some widely-studied algorithms to compare the performance IDS with the results of these algorithms. The aim of these algorithm is to minimize the Bayesian Regret for the specified problem.

In the MAB file we implement the generic class for a Multi Arm Bandit settings. We implemented the algorithms that are not problem specific and some useful function to handle the results of the algorithm.

We provide a few classes we settings and functions to run experiments for the problem:

• FiniteSets: A first algorithm for the case where the parameter space, action space and outcome space are all finites
• BernoulliMAB: independent Bernoulli arms with parameters drawn uniformly at random.
• GaussianMAB: independent Gaussian arms where the mean is drawn at random with the gaussian distribution
• LinearMAB: Linear Gaussian bandit with independent features, this class does not use MAB but a specific class designed for Linear Bandits. All parameters are drawn from Gaussians in this model

We provide an expe file with functions that help to run experiments for the pre-cited problems easily. These function are called in the main file, which is designed to help the choice of the arguments in the expe functions.