11 citations · 26 across the 6 of their papers we have counts for
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cs.LG2023★ 4 cited
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…
cs.LG2022★ 2 cited
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…
cs.LG2021★ 2 cited
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…