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.

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