Broad Learning from Narrow Training: A Case Study in Robotic Soccer
Next: Introduction
Abstract:
The range of unseen instances that can be successfully classified by a
learning algorithm is determined not only by the distribution of the
training data, but also by the parameters of the function to be
learned. With the right parameters, learning in a certain region of
the state space can generalize to completely different areas with no
retraining. We demonstrate the power of using well-chosen inputs to
(and outputs from) neural networks by conducting experiments in our
robotic soccer domain. We train an agent to shoot a moving ball into
a goal in a specific situation and end up with a general shooting
behavior that is much more widely applicable.
Keywords:
Robotic Soccer, Neural Networks, Flexible Inputs
Tech Report Number:
CMU-CS-95-207
Broad Learning from Narrow Training: A Case Study in Robotic Soccer
Peter Stone and Manuela Veloso
Peter Stone
Wed Nov 8 14:49:26 EST 1995