Transfer Learning via Relational Templates
نویسندگان
چکیده
Transfer Learning refers to learning of knowledge in one domain that can be applied to a different domain. In this paper, we view transfer learning as generalization of knowledge in a richer representation language that includes multiple subdomains as parts of the same superdomain. We employ relational templates of different specificity to learn pieces of additive value functions. We show significant transfer of learned knowledge across different subdomains of a real-time strategy game by generalizing the value function using relational templates.
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تاریخ انتشار 2009