2 citations · 2 across the 1 of their papers we have counts for
5 papers
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…
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…
Importance Resampling for Off-policy Prediction
Matthew Schlegel, Wesley Chung, Daniel Graves +2
Importance sampling (IS) is a common reweighting strategy for off-policy prediction in reinforcement learning. While it is consistent and unbiased, it can result in high variance u…
Context-Dependent Upper-Confidence Bounds for Directed Exploration
Raksha Kumaraswamy, Matthew Schlegel, Adam White +1
Directed exploration strategies for reinforcement learning are critical for learning an optimal policy in a minimal number of interactions with the environment. Many algorithms use…
General Value Function Networks
Matthew Schlegel, Andrew Jacobsen, Zaheer Abbas +3
State construction is important for learning in partially observable environments. A general purpose strategy for state construction is to learn the state update using a Recurrent…