Guiding a Reinforcement Learner with Natural Language Advice: Initial Results in RoboCup Soccer

Gregory Kuhlmann, Peter Stone, Raymond Mooney, and Jude Shavlik

We describe our current efforts towards creating a reinforcement learner that learns both from reinforcements provided by its environment and from human-generated advice. Our research involves two complementary components: (a) mapping advice expressed in English to a formal advice language and (b) using advice expressed in a formal notation in a reinforcement learner. We use a subtask of the challenging RoboCup simulated soccer task as our testbed.


This page is copyrighted by AAAI. All rights reserved. Your use of this site constitutes acceptance of all of AAAI's terms and conditions and privacy policy.