97 citations · 172 across the 7 of their papers we have counts for
7 papers
Learning Successor Features the Simple Way
Raymond Chua, Arna Ghosh, Christos Kaplanis +2
In Deep Reinforcement Learning (RL), it is a challenge to learn representations that do not exhibit catastrophic forgetting or interference in non-stationary environments. Successo…
Encoders and Ensembles for Task-Free Continual Learning
Murray Shanahan, Christos Kaplanis, Jovana Mitrović
We present an architecture that is effective for continual learning in an especially demanding setting, where task boundaries do not exist or are unknown, and where classes have to…
Continual Reinforcement Learning with Multi-Timescale Replay
Christos Kaplanis, Claudia Clopath, Murray Shanahan
In this paper, we propose a multi-timescale replay (MTR) buffer for improving continual learning in RL agents faced with environments that are changing continuously over time at ti…
An Explicitly Relational Neural Network Architecture
Murray Shanahan, Kyriacos Nikiforou, Antonia Creswell +3
With a view to bridging the gap between deep learning and symbolic AI, we present a novel end-to-end neural network architecture that learns to form propositional representations w…
Policy Consolidation for Continual Reinforcement Learning
Christos Kaplanis, Murray Shanahan, Claudia Clopath
We propose a method for tackling catastrophic forgetting in deep reinforcement learning that is \textit{agnostic} to the timescale of changes in the distribution of experiences, do…
Continual Reinforcement Learning with Complex Synapses
Christos Kaplanis, Murray Shanahan, Claudia Clopath
Unlike humans, who are capable of continual learning over their lifetimes, artificial neural networks have long been known to suffer from a phenomenon known as catastrophic forgett…
Feature Control as Intrinsic Motivation for Hierarchical Reinforcement Learning
Nat Dilokthanakul, Christos Kaplanis, Nick Pawlowski +1
The problem of sparse rewards is one of the hardest challenges in contemporary reinforcement learning. Hierarchical reinforcement learning (HRL) tackles this problem by using a set…