no code implementations • ICLR 2022 • Hang Ren, Aivar Sootla, Taher Jafferjee, Junxiao Shen, Jun Wang, Haitham Bou-Ammar
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 (discontinuous) context changes occurring during an episode; and c) Markovian context evolution.
1 code implementation • 14 Feb 2022 • Aivar Sootla, Alexander I. Cowen-Rivers, Taher Jafferjee, Ziyan Wang, David Mguni, Jun Wang, Haitham Bou-Ammar
Satisfying safety constraints almost surely (or with probability one) can be critical for the deployment of Reinforcement Learning (RL) in real-life applications.
no code implementations • 3 Feb 2022 • Xihan Li, Xiang Chen, Rasul Tutunov, Haitham Bou-Ammar, Lei Wang, Jun Wang
The Schr\"odinger equation is at the heart of modern quantum mechanics.
no code implementations • 29 Jan 2022 • Asif Khan, Alexander I. Cowen-Rivers, Derrick-Goh-Xin Deik, Antoine Grosnit, Kamil Dreczkowski, Philippe A. Robert, Victor Greiff, Rasul Tutunov, Dany Bou-Ammar, Jun Wang, Haitham Bou-Ammar
Therefore, it is a priority to design optimal antigen-specific CDRH3 regions to develop therapeutic antibodies to combat harmful pathogens.
2 code implementations • 7 Jun 2021 • Antoine Grosnit, Rasul Tutunov, Alexandre Max Maraval, Ryan-Rhys Griffiths, Alexander I. Cowen-Rivers, Lin Yang, Lin Zhu, Wenlong Lyu, Zhitang Chen, Jun Wang, Jan Peters, Haitham Bou-Ammar
We introduce a method combining variational autoencoders (VAEs) and deep metric learning to perform Bayesian optimisation (BO) over high-dimensional and structured input spaces.
Ranked #1 on
Molecular Graph Generation
on ZINC
1 code implementation • 15 Dec 2020 • Antoine Grosnit, Alexander I. Cowen-Rivers, Rasul Tutunov, Ryan-Rhys Griffiths, Jun Wang, Haitham Bou-Ammar
Bayesian optimisation presents a sample-efficient methodology for global optimisation.
no code implementations • 10 Feb 2020 • Rasul Tutunov, Minne Li, Alexander I. Cowen-Rivers, Jun Wang, Haitham Bou-Ammar
In this paper, we present C-ADAM, the first adaptive solver for compositional problems involving a non-linear functional nesting of expected values.
no code implementations • 19 Feb 2018 • Garrett Andersen, Peter Vrancx, Haitham Bou-Ammar
A common approach to HL, is to provide the agent with a number of high-level skills that solve small parts of the overall problem.
no code implementations • 9 Feb 2018 • Jordi Grau-Moya, Felix Leibfried, Haitham Bou-Ammar
Within the context of video games the notion of perfectly rational agents can be undesirable as it leads to uninteresting situations, where humans face tough adversarial decision makers.
no code implementations • 6 Aug 2017 • Felix Leibfried, Jordi Grau-Moya, Haitham Bou-Ammar
Different learning outcomes can be demonstrated by tuning a Lagrange multiplier accordingly.