1 code implementation • 20 May 2024 • Eloi Alonso, Adam Jelley, Vincent Micheli, Anssi Kanervisto, Amos Storkey, Tim Pearce, François Fleuret
Motivated by this paradigm shift, we introduce DIAMOND (DIffusion As a Model Of eNvironment Dreams), a reinforcement learning agent trained in a diffusion world model.
no code implementations • 26 Feb 2024 • Tianjiao Luo, Tim Pearce, Huayu Chen, Jianfei Chen, Jun Zhu
Generative Adversarial Imitation Learning (GAIL) trains a generative policy to mimic a demonstrator.
no code implementations • 26 Oct 2023 • Stephen Mak, Liming Xu, Tim Pearce, Michael Ostroumov, Alexandra Brintrup
Our contribution is that we are the first to consider both the route allocation problem and gain sharing problem simultaneously - without access to the expensive characteristic function.
no code implementations • 26 Oct 2023 • Stephen Mak, Liming Xu, Tim Pearce, Michael Ostroumov, Alexandra Brintrup
Our contribution is that our decentralised approach is both scalable and considers the self-interested nature of companies.
1 code implementation • 15 Feb 2023 • Fanqi Lin, Shiyu Huang, Tim Pearce, Wenze Chen, Wei-Wei Tu
Multi-agent football poses an unsolved challenge in AI research.
1 code implementation • 25 Jan 2023 • Tim Pearce, Tabish Rashid, Anssi Kanervisto, Dave Bignell, Mingfei Sun, Raluca Georgescu, Sergio Valcarcel Macua, Shan Zheng Tan, Ida Momennejad, Katja Hofmann, Sam Devlin
This paper studies their application as observation-to-action models for imitating human behaviour in sequential environments.
1 code implementation • 12 Jul 2022 • Wentse Chen, Shiyu Huang, Yuan Chiang, Tim Pearce, Wei-Wei Tu, Ting Chen, Jun Zhu
We propose Diversity-Guided Policy Optimization (DGPO), an on-policy algorithm that discovers multiple strategies for solving a given task.
1 code implementation • 26 May 2022 • Tim Pearce, Jong-Hyeon Jeong, Yichen Jia, Jun Zhu
To offer theoretical insight into our algorithm, we show firstly that it can be interpreted as a form of expectation-maximisation, and secondly that it exhibits a desirable `self-correcting' property.
1 code implementation • 28 Jul 2021 • Bang Xiang Yong, Tim Pearce, Alexandra Brintrup
After an autoencoder (AE) has learnt to reconstruct one dataset, it might be expected that the likelihood on an out-of-distribution (OOD) input would be low.
no code implementations • 9 Jun 2021 • Tim Pearce, Alexandra Brintrup, Jun Zhu
It is often remarked that neural networks fail to increase their uncertainty when predicting on data far from the training distribution.
2 code implementations • 9 Apr 2021 • Tim Pearce, Jun Zhu
This paper describes an AI agent that plays the popular first-person-shooter (FPS) video game `Counter-Strike; Global Offensive' (CSGO) from pixel input.
1 code implementation • 12 Jul 2020 • Tim Pearce, Andrew Y. K. Foong, Alexandra Brintrup
This paper explores the benefits of adding structure to weight priors.
1 code implementation • 20 Feb 2020 • Russell Tsuchida, Tim Pearce, Chris van der Heide, Fred Roosta, Marcus Gallagher
Secondly, and more generally, we analyse the fixed-point dynamics of iterated kernels corresponding to a broad range of activation functions.
1 code implementation • 15 May 2019 • Tim Pearce, Russell Tsuchida, Mohamed Zaki, Alexandra Brintrup, Andy Neely
A simple, flexible approach to creating expressive priors in Gaussian process (GP) models makes new kernels from a combination of basic kernels, e. g. summing a periodic and linear kernel can capture seasonal variation with a long term trend.
no code implementations • 27 Nov 2018 • Tim Pearce, Mohamed Zaki, Andy Neely
Ensembles of neural networks (NNs) have long been used to estimate predictive uncertainty; a small number of NNs are trained from different initialisations and sometimes on differing versions of the dataset.
2 code implementations • 12 Oct 2018 • Tim Pearce, Felix Leibfried, Alexandra Brintrup, Mohamed Zaki, Andy Neely
Ensembling NNs provides an easily implementable, scalable method for uncertainty quantification, however, it has been criticised for not being Bayesian.
2 code implementations • 29 May 2018 • Tim Pearce, Nicolas Anastassacos, Mohamed Zaki, Andy Neely
The use of ensembles of neural networks (NNs) for the quantification of predictive uncertainty is widespread.
1 code implementation • ICML 2018 • Tim Pearce, Mohamed Zaki, Alexandra Brintrup, Andy Neely
This paper considers the generation of prediction intervals (PIs) by neural networks for quantifying uncertainty in regression tasks.