13 citations · 36 across the 9 of their papers we have counts for
6 papers · 1 filter
What Should I Know? Using Meta-gradient Descent for Predictive Feature Discovery in a Single Stream of Experience
Alexandra Kearney, Anna Koop, Johannes Günther +1
In computational reinforcement learning, a growing body of work seeks to construct an agent's perception of the world through predictions of future sensations; predictions about en…
Finding Useful Predictions by Meta-gradient Descent to Improve Decision-making
Alex Kearney, Anna Koop, Johannes Günther +1
In computational reinforcement learning, a growing body of work seeks to express an agent's model of the world through predictions about future sensations. In this manuscript we fo…
Examining the Use of Temporal-Difference Incremental Delta-Bar-Delta for Real-World Predictive Knowledge Architectures
Johannes Günther, Nadia M. Ady, Alex Kearney +2
Predictions and predictive knowledge have seen recent success in improving not only robot control but also other applications ranging from industrial process control to rehabilitat…
When is a Prediction Knowledge?
Alex Kearney, Patrick M. Pilarski
Within Reinforcement Learning, there is a growing collection of research which aims to express all of an agent's knowledge of the world through predictions about sensation, behavio…
Learning Feature Relevance Through Step Size Adaptation in Temporal-Difference Learning
Alex Kearney, Vivek Veeriah, Jaden Travnik +2
There is a long history of using meta learning as representation learning, specifically for determining the relevance of inputs. In this paper, we examine an instance of meta-learn…
TIDBD: Adapting Temporal-difference Step-sizes Through Stochastic Meta-descent
Alex Kearney, Vivek Veeriah, Jaden B. Travnik +2
In this paper, we introduce a method for adapting the step-sizes of temporal difference (TD) learning. The performance of TD methods often depends on well chosen step-sizes, yet fe…