21 citations · 29 across the 5 of their papers we have counts for
6 papers
Investigating Action Encodings in Recurrent Neural Networks in Reinforcement Learning
Matthew Schlegel, Volodymyr Tkachuk, Adam White +1
Building and maintaining state to learn policies and value functions is critical for deploying reinforcement learning (RL) agents in the real world. Recurrent neural networks (RNNs…
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…