21 citations · 78 across the 10 of their papers we have counts for
19 papers
Reinforcement Learning in Presence of Discrete Markovian Context Evolution
Hang Ren, Aivar Sootla, Taher Jafferjee +3
We consider a context-dependent Reinforcement Learning (RL) setting, which is characterized by: a) an unknown finite number of not directly observable contexts; b) abrupt (disconti…
Self-consistent Gradient-like Eigen Decomposition in Solving Schrödinger Equations
Xihan Li, Xiang Chen, Rasul Tutunov +3
The Schrödinger equation is at the heart of modern quantum mechanics. Since exact solutions of the ground state are typically intractable, standard approaches approximate Schröding…
Efficient and Reactive Planning for High Speed Robot Air Hockey
Puze Liu, Davide Tateo, Haitham Bou-Ammar +1
Highly dynamic robotic tasks require high-speed and reactive robots. These tasks are particularly challenging due to the physical constraints, hardware limitations, and the high un…
High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning
Antoine Grosnit, Rasul Tutunov, Alexandre Max Maraval +9
We introduce a method combining variational autoencoders (VAEs) and deep metric learning to perform Bayesian optimisation (BO) over high-dimensional and structured input spaces. By…
Are we Forgetting about Compositional Optimisers in Bayesian Optimisation?
Antoine Grosnit, Alexander I. Cowen-Rivers, Rasul Tutunov +3
Bayesian optimisation presents a sample-efficient methodology for global optimisation. Within this framework, a crucial performance-determining subroutine is the maximisation of th…
SAMBA: Safe Model-Based & Active Reinforcement Learning
Alexander I. Cowen-Rivers, Daniel Palenicek, Vincent Moens +4
In this paper, we propose SAMBA, a novel framework for safe reinforcement learning that combines aspects from probabilistic modelling, information theory, and statistics. Our metho…