Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow

2 code implementations • 16 Feb 2023 • Victor Picheny, Joel Berkeley, Henry B. Moss, Hrvoje Stojic, Uri Granta, Sebastian W. Ober, Artem Artemev, Khurram Ghani, Alexander Goodall, Andrei Paleyes, Sattar Vakili, Sergio Pascual-Diaz, Stratis Markou, Jixiang Qing, Nasrulloh R. B. S Loka, Ivo Couckuyt

We present Trieste, an open-source Python package for Bayesian optimization and active learning benefiting from the scalability and efficiency of TensorFlow.

Active Learning Bayesian Optimization +2

Bellman: A Toolbox for Model-Based Reinforcement Learning in TensorFlow

2 code implementations • 26 Mar 2021 • John McLeod, Hrvoje Stojic, Vincent Adam, Dongho Kim, Jordi Grau-Moya, Peter Vrancx, Felix Leibfried

This paves the way for new research directions, e. g. investigating uncertainty-aware environment models that are not necessarily neural-network-based, or developing algorithms to solve industrially-motivated benchmarks that share characteristics with real-world problems.

Model-based Reinforcement Learning reinforcement-learning +1

An empirical evaluation of active inference in multi-armed bandits

1 code implementation • 21 Jan 2021 • Dimitrije Markovic, Hrvoje Stojic, Sarah Schwoebel, Stefan J. Kiebel

This comparison is done on two types of bandit problems: a stationary and a dynamic switching bandit.

BIG-bench Machine Learning Decision Making +3

Diversity of preferences can increase collective welfare in sequential exploration problems

no code implementations • 28 Mar 2017 • Pantelis P. Analytis, Hrvoje Stojic, Alexandros Gelastopoulos, Mehdi Moussaïd

It leads to a consistent increase in the quality of the consumed alternatives that outweighs the increased cost of search for the users.