2 citations · 2 across the 1 of their papers we have counts for
3 papers
cs.LG2022★ 2 cited
Continual Auxiliary Task Learning
Matthew McLeod, Chunlok Lo, Matthew Schlegel +4
Learning auxiliary tasks, such as multiple predictions about the world, can provide many benefits to reinforcement learning systems. A variety of off-policy learning algorithms hav…
cs.LG2021
Parameter-free Gradient Temporal Difference Learning
Andrew Jacobsen, Alan Chan
Reinforcement learning lies at the intersection of several challenges. Many applications of interest involve extremely large state spaces, requiring function approximation to enabl…
cs.LG2019
Meta-descent for Online, Continual Prediction
Andrew Jacobsen, Matthew Schlegel, Cameron Linke +3
This paper investigates different vector step-size adaptation approaches for non-stationary online, continual prediction problems. Vanilla stochastic gradient descent can be consid…