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.
Satisfying safety constraints almost surely (or with probability one) can be critical for the deployment of Reinforcement Learning (RL) in real-life applications.
1 code implementation • 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
Bayesian optimisation presents a sample-efficient methodology for global optimisation.
In this paper, we present C-ADAM, the first adaptive solver for compositional problems involving a non-linear functional nesting of expected values.
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.
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.