98 citations · 246 across the 18 of their papers we have counts for
3 papers · 1 filter
How Should an Agent Practice?
Janarthanan Rajendran, Richard Lewis, Vivek Veeriah +2
We present a method for learning intrinsic reward functions to drive the learning of an agent during periods of practice in which extrinsic task rewards are not available. During p…
Discovery of Useful Questions as Auxiliary Tasks
Vivek Veeriah, Matteo Hessel, Zhongwen Xu +6
Arguably, intelligent agents ought to be able to discover their own questions so that in learning answers for them they learn unanticipated useful knowledge and skills; this depart…
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…