(Georgia Institute of Technology, 2013)
Cobo, Luis C.; Isbell, Charles L.; Thomaz, Andrea L.
We present Object Focused Q-learning (OF-Q), a novel reinforcement learning algorithm that can offer exponential speed-ups over classic Q-learning on domains composed of independent objects. An OF-Q agent treats the state space as a collection of objects organized into different object classes. Our key contribution is a control policy that uses non-optimal Q-functions to estimate the risk of ignoring parts of the state space. We compare our algorithm to traditional Q-learning and previous arbitration algorithms in two domains, including a version of Space Invaders.
(Georgia Institute of Technology, 2010)
Zang, Peng; Tian, Runhe; Thomaz, Andrea L.; Isbell, Charles L.
Agents that operate in human environments will
need to be able to learn new skills from everyday people.
Learning from demonstration (LfD) is a popular paradigm for
this. Drawing from our interest in Socially Guided Machine
Learning, we explore the impact of interactivity on learning
from demonstration. We present findings from a study with
human subjects showing people who are able to interact with
the learning agent provide better demonstrations (in part) by
adapting based on learner performance which results in improved
learning performance. We also find that interactivity increases a
sense of engagement and may encourage players to participate
longer. Our exploration of interactivity sheds light on how best
to obtain demonstrations for LfD applications.