2 code implementations • NeurIPS 2023 • Michael Painter, Mohamed Baioumy, Nick Hawes, Bruno Lacerda
Monte-Carlo Tree Search (MCTS) methods, such as Upper Confidence Bound applied to Trees (UCT), are instrumental to automated planning techniques.
2 code implementations • 16 Nov 2023 • Alexander Rutherford, Benjamin Ellis, Matteo Gallici, Jonathan Cook, Andrei Lupu, Gardar Ingvarsson, Timon Willi, Akbir Khan, Christian Schroeder de Witt, Alexandra Souly, Saptarashmi Bandyopadhyay, Mikayel Samvelyan, Minqi Jiang, Robert Tjarko Lange, Shimon Whiteson, Bruno Lacerda, Nick Hawes, Tim Rocktaschel, Chris Lu, Jakob Nicolaus Foerster
This not only enables GPU acceleration, but also provides a more flexible MARL environment, unlocking the potential for self-play, meta-learning, and other future applications in MARL.
no code implementations • 2 Jul 2023 • Daniel Omeiza, Raunak Bhattacharyya, Nick Hawes, Marina Jirotka, Lars Kunze
In this paper, we investigate the effects of natural language explanations' specificity on passengers in autonomous driving.
1 code implementation • 15 Jun 2023 • Marc Rigter, Bruno Lacerda, Nick Hawes
In this setting we address reinforcement learning, and learning from demonstration, where there is a cost associated with human time.
no code implementations • 6 Feb 2023 • Branton DeMoss, Paul Duckworth, Nick Hawes, Ingmar Posner
We propose DITTO, an offline imitation learning algorithm which uses world models and on-policy reinforcement learning to addresses the problem of covariate shift, without access to an oracle or any additional online interactions.
1 code implementation • NeurIPS 2023 • Marc Rigter, Bruno Lacerda, Nick Hawes
Our model-based approach is risk-averse to both epistemic and aleatoric uncertainty.
2 code implementations • 26 Apr 2022 • Marc Rigter, Bruno Lacerda, Nick Hawes
Model-based algorithms, which learn a model of the environment from the dataset and perform conservative policy optimisation within that model, have emerged as a promising approach to this problem.
no code implementations • 17 Apr 2022 • Mohamed Baioumy, William Hartemink, Riccardo M. G. Ferrari, Nick Hawes
Model-based fault-tolerant control (FTC) often consists of two distinct steps: fault detection & isolation (FDI), and fault accommodation.
1 code implementation • 25 Oct 2021 • Marc Rigter, Paul Duckworth, Bruno Lacerda, Nick Hawes
This motivates us to propose a lexicographic approach which minimises the expected cost subject to the constraint that the CVaR of the total cost is optimal.
no code implementations • 13 Sep 2021 • Mohamed Baioumy, Bruno Lacerda, Paul Duckworth, Nick Hawes
Previous work on planning as active inference addresses finite horizon problems and solutions valid for online planning.
no code implementations • 13 Sep 2021 • Mohamed Baioumy, Corrado Pezzato, Carlos Hernandez Corbato, Nick Hawes, Riccardo Ferrari
This work presents a fault-tolerant control scheme for sensory faults in robotic manipulators based on active inference.
no code implementations • NeurIPS 2021 • Marc Rigter, Bruno Lacerda, Nick Hawes
In this work, we address risk-averse Bayes-adaptive reinforcement learning.
no code implementations • 8 Dec 2020 • Marc Rigter, Bruno Lacerda, Nick Hawes
We propose a dynamic programming algorithm that utilises the regret Bellman equation, and show that it optimises minimax regret exactly for UMDPs with independent uncertainties.
1 code implementation • 12 May 2020 • Mohamed Baioumy, Paul Duckworth, Bruno Lacerda, Nick Hawes
This work presents an approach for control, state-estimation and learning model (hyper)parameters for robotic manipulators.
Robotics
no code implementations • 9 Mar 2020 • Michael Painter, Bruno Lacerda, Nick Hawes
This work investigates Monte-Carlo planning for agents in stochastic environments, with multiple objectives.
no code implementations • 13 Jul 2018 • Lars Kunze, Nick Hawes, Tom Duckett, Marc Hanheide, Tomáš Krajník
Autonomous systems will play an essential role in many applications across diverse domains including space, marine, air, field, road, and service robotics.
no code implementations • 7 Mar 2018 • Fatma Faruq, Bruno Lacerda, Nick Hawes, David Parker
We propose novel techniques for task allocation and planning in multi-robot systems operating in uncertain environments.
1 code implementation • IEEE Xplore: 2017 • Nizar Massouh, Francesca Babiloni, Tatiana Tommasi, Jay Young, Nick Hawes, Barbara Caputo
We contribute to this research thread with two findings: (1) a study correlating a given level of noisily labels to the expected drop in accuracy, for two deep architectures, on two different types of noise, that clearly identifies GoogLeNet as a suitable architecture for learning from Web data; (2) a recipe for the creation of Web datasets with minimal noise and maximum visual variability, based on a visual and natural language processing concept expansion strategy.