The areas of reinforcement learning and multi-armed bandits have recently seen significant innovation, while many application domains, such as e-commerce, are full of problems and challenges to which vanilla RL or MAB methods cannot directly apply. This workshop aims at filling this communication gap by creating a platform for researchers and practitioners from both the method/theory side and application side of the community. Having this platform now instead of at a later time is beneficial to all sides of the community: practitioners and frontline scientists are able to avoid re-inventing existing techniques; theory-oriented researchers can find motivation in industry problems, working within more realistic settings, and making real-world impact. The 2nd Multi-armed Bandits and Reinforcement Learning Workshop was a half day workshop co-located with the 29th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (KDD 2023) in Long Beach, California.

Black-box heterogeneous treatment effect (HTE) models are increasingly being used to create personalized policies that assign individuals to their optimal treatments. However, they are difficult to understand, and can be burdensome to maintain in a production environment. In this paper, we present a scalable, interpretable personalized experimentation system, implemented and deployed in production at Meta. The system works in a multiple treatment, multiple outcome setting typical at Meta to: (1) learn explanations for black-box HTE models; (2) generate interpretable personalized policies. We evaluate the methods used in the system on publicly available data and Meta use cases, and discuss lessons learnt during the development of the system.

The areas of reinforcement learning and multi-armed bandits have recently seen significant innovation, while many application domains, such as e-commerce, are full of problems and challenges to which vanilla RL or MAB methods cannot directly apply. This workshop aims at filling this communication gap by creating a platform for researchers and practitioners from both the method/theory side and application side of the community. Having this platform now instead of at a later time is beneficial to all sides of the community: practitioners and frontline scientists are able to avoid re-inventing existing techniques; theory-oriented researchers can find motivation in industry problems, working within more realistic settings, and making real-world impact. The 1st Multi-armed Bandits and Reinforcement Learning Workshop was a full day workshop co-located with the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (KDD 2021) in Singapore.