4 citations · 12 across the 9 of their papers we have counts for
9 papers
Hierarchical Successor Representation for Robust Transfer
Changmin Yu, Máté Lengyel
The successor representation (SR) provides a powerful framework for decoupling predictive dynamics from rewards, enabling rapid generalisation across reward configurations. However…
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…
Unsupervised representation learning with recognition-parametrised probabilistic models
William I. Walker, Hugo Soulat, Changmin Yu +1
We introduce a new approach to probabilistic unsupervised learning based on the recognition-parametrised model (RPM): a normalised semi-parametric hypothesis class for joint distri…
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…
SEREN: Knowing When to Explore and When to Exploit
Changmin Yu, David Mguni, Dong Li +3
Efficient reinforcement learning (RL) involves a trade-off between "exploitative" actions that maximise expected reward and "explorative'" ones that sample unvisited states. To enc…
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…
DESTA: A Framework for Safe Reinforcement Learning with Markov Games of Intervention
David Mguni, Usman Islam, Yaqi Sun +7
Reinforcement learning (RL) involves performing exploratory actions in an unknown system. This can place a learning agent in dangerous and potentially catastrophic system states. C…
What About Inputing Policy in Value Function: Policy Representation and Policy-extended Value Function Approximator
Hongyao Tang, Zhaopeng Meng, Jianye Hao +9
We study Policy-extended Value Function Approximator (PeVFA) in Reinforcement Learning (RL), which extends conventional value function approximator (VFA) to take as input not only…
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…