Self-Adjusting Bayesian Optimization with SAWEI

By Carolin Benjamins, Elena Raponi, Anja Jankovic, Carola Doerr and Marius Lindauer TLDR: In BO: We self-adjust the exploration-exploitation trade-off online in the acquisition function, adapting to any problem landscape. Motivation Bayesian optimization (BO) encompasses a class of surrogate-based, sample-efficient algorithms for optimizing black-box problems with small evaluation budgets. However, BO itself has numerous design […]

Read More

CARL: A benchmark to study generalization in Reinforcement Learning

TL;DR: CARL is a benchmark for contextual RL (cRL). In cRL, we aim to generalize over different contexts. In CARL we saw that if we vary the context, the learning becomes more difficult, and making the context explicit can facilitate learning. CARL makes the context defining the behavior of the environment visible and configurable. This […]

Read More