no code implementations • 13 Apr 2024 • Puze Liu, Haitham Bou-Ammar, Jan Peters, Davide Tateo
Videos of the real robot experiments are available on the project website (https://puzeliu. github. io/TRO-ATACOM).
no code implementations • 20 Feb 2024 • Adam X. Yang, Maxime Robeyns, Thomas Coste, Jun Wang, Haitham Bou-Ammar, Laurence Aitchison
To ensure that large language model (LLM) responses are helpful and non-toxic, we usually fine-tune a reward model on human preference data.
no code implementations • 22 Dec 2023 • Filippos Christianos, Georgios Papoudakis, Matthieu Zimmer, Thomas Coste, Zhihao Wu, Jingxuan Chen, Khyati Khandelwal, James Doran, Xidong Feng, Jiacheng Liu, Zheng Xiong, Yicheng Luo, Jianye Hao, Kun Shao, Haitham Bou-Ammar, Jun Wang
This paper presents a general framework model for integrating and learning structured reasoning into AI agents' policies.
no code implementations • 20 Oct 2023 • Rasul Tutunov, Antoine Grosnit, Juliusz Ziomek, Jun Wang, Haitham Bou-Ammar
This paper delves into the capabilities of large language models (LLMs), specifically focusing on advancing the theoretical comprehension of chain-of-thought prompting.
2 code implementations • 30 Jan 2023 • Juliusz Ziomek, Haitham Bou-Ammar
Learning decompositions of expensive-to-evaluate black-box functions promises to scale Bayesian optimisation (BO) to high-dimensional problems.
no code implementations • 29 Jan 2023 • Vahan Arsenyan, Antoine Grosnit, Haitham Bou-Ammar
Causal Bayesian optimisation (CaBO) combines causality with Bayesian optimisation (BO) and shows that there are situations where the optimal reward is not achievable if causal knowledge is ignored.
1 code implementation • 11 Jan 2023 • Piotr Kicki, Puze Liu, Davide Tateo, Haitham Bou-Ammar, Krzysztof Walas, Piotr Skrzypczyński, Jan Peters
Motion planning is a mature area of research in robotics with many well-established methods based on optimization or sampling the state space, suitable for solving kinematic motion planning.
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 • 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.
1 code implementation • 29 Jan 2022 • Asif Khan, Alexander I. Cowen-Rivers, Antoine Grosnit, Derrick-Goh-Xin Deik, Philippe A. Robert, Victor Greiff, Eva Smorodina, Puneet Rawat, Kamil Dreczkowski, Rahmad Akbar, Rasul Tutunov, Dany Bou-Ammar, Jun Wang, Amos Storkey, Haitham Bou-Ammar
software suite as a black-box oracle to score the target specificity and affinity of designed antibodies \textit{in silico} in an unconstrained fashion~\citep{robert2021one}.
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.