14 citations · 29 across the 17 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 End-to-End Goal-Oriented Dialog with Maximal User Task Success and Minimal Human Agent Use
Janarthanan Rajendran, Jatin Ganhotra, Lazaros Polymenakos
Neural end-to-end goal-oriented dialog systems showed promise to reduce the workload of human agents for customer service, as well as reduce wait time for users. However, their ina…