We consider the problem of fitting a reinforcement learning (RL) model to some given behavioral data under a multi-armed bandit environment. These models have received much attention in recent years ...

Abstract: Contextual multi-armed bandit algorithms serve as an effective technique to address online sequential decision-making problems. Despite their popularity, when it comes to off-the-shelf tools ...

Joe Grantham is a contributor from the UK with a degree in Classical Studies. His love for gaming is only rivaled by a deep passion for medieval history, which often seeps into his articles. With over ...

Gender-based violence and discrimination are persistent across societies, and rates across Palestine were already unacceptable before the current crisis. The toll of armed conflict is felt heavily ...

The multi-armed bandit (MAB) problem models a decision-maker that optimizes its actions based on current and acquired new knowledge to maximize its reward. This type of online decision is prominent in ...

Mab2Rec (AAAI'24) is a Python framework for building bandit-based recommendation algorithms. It supports context-free, parametric and non-parametric contextual bandit models. It is designed to enable ...