no code implementations • 10 Sep 2022 • Alexander I. Cowen-Rivers, Philip John Gorinski, Aivar Sootla, Asif Khan, Liu Furui, Jun Wang, Jan Peters, Haitham Bou Ammar
Optimizing combinatorial structures is core to many real-world problems, such as those encountered in life sciences.
1 code implementation • 6 Jun 2022 • Aivar Sootla, Alexander I. Cowen-Rivers, Jun Wang, Haitham Bou Ammar
We further show that Simmer can stabilize training and improve the performance of safe RL with average constraints.
no code implementations • 31 May 2022 • David Mguni, Aivar Sootla, Juliusz Ziomek, Oliver Slumbers, Zipeng Dai, Kun Shao, Jun Wang
In this paper, we introduce a reinforcement learning (RL) framework named \textbf{L}earnable \textbf{I}mpulse \textbf{C}ontrol \textbf{R}einforcement \textbf{A}lgorithm (LICRA), for learning to optimally select both when to act and which actions to take when actions incur costs.
no code implementations • 30 May 2022 • Changmin Yu, David Mguni, Dong Li, Aivar Sootla, Jun Wang, Neil Burgess
Efficient reinforcement learning (RL) involves a trade-off between "exploitative" actions that maximise expected reward and "explorative'" ones that sample unvisited states.
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 • 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.
no code implementations • 27 Oct 2021 • David Mguni, Usman Islam, Yaqi Sun, Xiuling Zhang, Joel Jennings, Aivar Sootla, Changmin Yu, Ziyan Wang, Jun Wang, Yaodong Yang
In this paper, we introduce a new generation of RL solvers that learn to minimise safety violations while maximising the task reward to the extent that can be tolerated by the safe policy.
no code implementations • 6 Jul 2021 • Vincent Moens, Aivar Sootla, Haitham Bou Ammar, Jun Wang
We present a method for conditional sampling for pre-trained normalizing flows when only part of an observation is available.
no code implementations • 10 Oct 2020 • Thomas Tanay, Aivar Sootla, Matteo Maggioni, Puneet K. Dokania, Philip Torr, Ales Leonardis, Gregory Slabaugh
Recurrent models are a popular choice for video enhancement tasks such as video denoising or super-resolution.
1 code implementation • 12 Jun 2020 • Alexander I. Cowen-Rivers, Daniel Palenicek, Vincent Moens, Mohammed Abdullah, Aivar Sootla, Jun Wang, Haitham Ammar
In this paper, we propose SAMBA, a novel framework for safe reinforcement learning that combines aspects from probabilistic modelling, information theory, and statistics.
no code implementations • 28 Mar 2014 • Wei Pan, Aivar Sootla, Guy-Bart Stan
In this paper, we present a distributed algorithm for the reconstruction of large-scale nonlinear networks.
no code implementations • 12 Mar 2013 • Aivar Sootla, Natalja Strelkowa, Damien Ernst, Mauricio Barahona, Guy-Bart Stan
In this paper, we consider the problem of optimal exogenous control of gene regulatory networks.