Embedded Teaching of Reinforcement Learners
نویسندگان
چکیده
Knowledge plays an important role in an agent's ability to perform well in its environment. Teaching can be used to improve an agent's performance by enhancing its knowledge. We propose a speci c model of teaching, which we call embedded teaching. An embedded teacher is an agent situated with a less knowledgeable \student" in a common environment. The teacher's goal is to lead the student to adopt a particular desired behavior. The teacher's ability to teach is a ected by the dynamics of the common environment and may be limited by a restricted repertoire of actions or uncertainty about the outcome of actions; we explicitly represent these limitations as part of our model. In this paper, we address a number of theoretical issues including the characterization of a challenging embedded teaching domain and the computation of optimal teaching policies. We then incorporate these ideas in a series of experiments designed to evaluate our ability to teach two types of reinforcement learners.
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