چکیده (انگلیسی):

Developing general purpose algorithms for learning an accurate model of dynamical systems from example
traces of the system is still a challenging research problem. Predictive State Representation (PSR) models
represent the state of a dynamical system as a set of predictions about future events. Our work focuses on
improving Temporal Difference Networks (TD Nets), a general class of predictive state models. We adapt
the internal structure of the TD Net and we present an improved algorithm for learning a TD Net model
from experience in the environment. The new algorithm accepts a set of known facts about the environment
and uses those facts to accelerate the learning. These facts can come from another learning algorithm (as in
this study) or from a designer’s prior knowledge about the environment. Experiments demonstrate that
using the new structure and learning algorithm improves the accuracy of the TD Net models. When tested in
an in finite environment, our new algorithm outperforms all of the standard PSR learning algorithms.