11 citations · 12 across the 6 of their papers we have counts for
4 papers · 1 filter
All You Need Is Supervised Learning: From Imitation Learning to Meta-RL With Upside Down RL
Kai Arulkumaran, Dylan R. Ashley, Jürgen Schmidhuber +1
Upside down reinforcement learning (UDRL) flips the conventional use of the return in the objective function in RL upside down, by taking returns as input and predicting actions. U…
Learning Relative Return Policies With Upside-Down Reinforcement Learning
Dylan R. Ashley, Kai Arulkumaran, Jürgen Schmidhuber +1
Lately, there has been a resurgence of interest in using supervised learning to solve reinforcement learning problems. Recent work in this area has largely focused on learning comm…
Does the Adam Optimizer Exacerbate Catastrophic Forgetting?
Dylan R. Ashley, Sina Ghiassian, Richard S. Sutton
Catastrophic forgetting remains a severe hindrance to the broad application of artificial neural networks (ANNs), however, it continues to be a poorly understood phenomenon. Despit…
Universal Successor Features for Transfer Reinforcement Learning
Chen Ma, Dylan R. Ashley, Junfeng Wen +1
Transfer in Reinforcement Learning (RL) refers to the idea of applying knowledge gained from previous tasks to solving related tasks. Learning a universal value function (Schaul et…