9 citations · 14 across the 3 of their papers we have counts for
3 papers
Learning Portable Representations for High-Level Planning
Steven James, Benjamin Rosman, George Konidaris
We present a framework for autonomously learning a portable representation that describes a collection of low-level continuous environments. We show that these abstract representat…
Reasoning about Unforeseen Possibilities During Policy Learning
Craig Innes, Alex Lascarides, Stefano V Albrecht +2
Methods for learning optimal policies in autonomous agents often assume that the way the domain is conceptualised---its possible states and actions and their causal structure---is…
Hierarchical Subtask Discovery With Non-Negative Matrix Factorization
Adam C. Earle, Andrew M. Saxe, Benjamin Rosman
Hierarchical reinforcement learning methods offer a powerful means of planning flexible behavior in complicated domains. However, learning an appropriate hierarchical decomposition…