11 citations · 12 across the 6 of their papers we have counts for
8 papers
Upside-Down Reinforcement Learning Can Diverge in Stochastic Environments With Episodic Resets
Miroslav Štrupl, Francesco Faccio, Dylan R. Ashley +2
Upside-Down Reinforcement Learning (UDRL) is an approach for solving RL problems that does not require value functions and uses only supervised learning, where the targets for give…
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…
Automatic Embedding of Stories Into Collections of Independent Media
Dylan R. Ashley, Vincent Herrmann, Zachary Friggstad +2
We look at how machine learning techniques that derive properties of items in a collection of independent media can be used to automatically embed stories into such collections. To…
Back to Square One: Superhuman Performance in Chutes and Ladders Through Deep Neural Networks and Tree Search
Dylan Ashley, Anssi Kanervisto, Brendan Bennett
We present AlphaChute: a state-of-the-art algorithm that achieves superhuman performance in the ancient game of Chutes and Ladders. We prove that our algorithm converges to the Nas…
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…