no code implementations • NeurIPS 2023 • Changmin Yu, Neil Burgess, Maneesh Sahani, Samuel J. Gershman
Here we focus on exploration with intrinsic rewards, where the agent transiently augments the external rewards with self-generated intrinsic rewards.
2 code implementations • 12 Sep 2022 • Paulius Micikevicius, Dusan Stosic, Neil Burgess, Marius Cornea, Pradeep Dubey, Richard Grisenthwaite, Sangwon Ha, Alexander Heinecke, Patrick Judd, John Kamalu, Naveen Mellempudi, Stuart Oberman, Mohammad Shoeybi, Michael Siu, Hao Wu
FP8 is a natural progression for accelerating deep learning training inference beyond the 16-bit formats common in modern processors.
1 code implementation • 12 Sep 2022 • Changmin Yu, Hugo Soulat, Neil Burgess, Maneesh Sahani
A key goal of unsupervised learning is to go beyond density estimation and sample generation to reveal the structure inherent within observed data.
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 • ICLR 2022 • Changmin Yu, Dong Li, Jianye Hao, Jun Wang, Neil Burgess
We propose learning via retracing, a novel self-supervised approach for learning the state representation (and the associated dynamics model) for reinforcement learning tasks.
no code implementations • ICLR Workshop SSL-RL 2021 • Changmin Yu, Dong Li, Hangyu Mao, Jianye Hao, Neil Burgess
Representation learning is a popular approach for reinforcement learning (RL) tasks with partially observable Markov decision processes.
1 code implementation • ICLR 2021 • Changmin Yu, Timothy E. J. Behrens, Neil Burgess
Knowing how the effects of directed actions generalise to new situations (e. g. moving North, South, East and West, or turning left, right, etc.)
1 code implementation • NeurIPS 2019 • Talfan Evans, Neil Burgess
Constructing and maintaining useful representations of sensory experience is essential for reasoning about ones environment.
no code implementations • 6 Oct 2019 • Jesse P. Geerts, Kimberly L. Stachenfeld, Neil Burgess
The effectiveness of Reinforcement Learning (RL) depends on an animal's ability to assign credit for rewards to the appropriate preceding stimuli.