We describe a reinforcement learning system that transfers skills from a previously learned source task to a related target task. The system uses inductive logic programming to analyze experience in the source task, and transfers rules for when to take actions. The target task learner accepts these rules through an advice-taking algorithm, which allows learners to benefit from outside guidance that may be imperfect. Our system accepts a human-provided mapping, which specifies the similarities between the source and target tasks and may also include advice about the differences between them. Using three tasks in the RoboCup simulated soccer domain, we demonstrate that this system can speed up reinforcement learning substantially. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Torrey, L., Shavlik, J., Walker, T., & Maclin, R. (2006). Skill acquisition via transfer learning and advice taking. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4212 LNAI, pp. 425–436). Springer Verlag. https://doi.org/10.1007/11871842_41
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