11 citations · 26 across the 6 of their papers we have counts for
6 papers
Successor-Predecessor Intrinsic Exploration
Changmin Yu, Neil Burgess, Maneesh Sahani +1
Exploration is essential in reinforcement learning, particularly in environments where external rewards are sparse. Here we focus on exploration with intrinsic rewards, where the a…
Structured Recognition for Generative Models with Explaining Away
Changmin Yu, Hugo Soulat, Neil Burgess +1
A key goal of unsupervised learning is to go beyond density estimation and sample generation to reveal the structure inherent within observed data. Such structure can be expressed…
Learning State Representations via Retracing in Reinforcement Learning
Changmin Yu, Dong Li, Jianye Hao +2
We propose learning via retracing, a novel self-supervised approach for learning the state representation (and the associated dynamics model) for reinforcement learning tasks. In a…
Prediction and Generalisation over Directed Actions by Grid Cells
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.) is key to rapid generalisation acro…
Probabilistic Successor Representations with Kalman Temporal Differences
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. One aspect of understanding the n…
Spatially Periodic Cells Are Neither Formed From Grids Nor Poor Isolation
Julija Krupic, Neil Burgess, John O'Keefe
Grid cells recorded in the parahippocampal formation of freely moving rodents provide a strikingly periodic representation of self-location whose underlying mechanism has been the…