Extended-Abstract

Using NEAT to Learn Operators for Flexible Boolean Composition within Reinforcement Learning
Skill composition is a growing area of interest within Reinforcement Learning (RL) research. For example, if designing a robot for …
Amir Esterhuysen, Steven James, Geraud Nangue Tasse, Benjamin Rosman, Jonathan Shock
PDF Cite
Accounting for the Sequential Nature of States to Learn Representations in Reinforcement Learning
In this work, we investigate the properties of data that cause popular representation learning approaches to fail. In particular, we …
Nathan Michlo, Devon Jarvis, Richard Klein, Steven James
PDF Cite
Adaptive Online Value Function Approximation with Wavelets
Using function approximation to represent a value function is necessary for continuous and high-dimensional state spaces. Linear …
Michael Beukman, Michael Mitcheley, Dean Wookey, Steven James, George Konidaris
PDF Cite
Learning Abstract and Transferable Representations for Planning
We are concerned with the question of how an agent can acquire its own representations from sensory data. We restrict our focus to …
Steven James, Benjamin Rosman, George Konidaris
PDF Cite
World Value Functions: Knowledge Representation for Multitask Reinforcement Learning
An open problem in artificial intelligence is how to learn and represent knowledge that is sufficient for a general agent that needs to …
Geraud Nangue Tasse, Benjamin Rosman, Steven James
PDF Cite