Poster Spotlights: Hierarchical Skill Learning for High-Level Planning

author: James MacGlashan, Department of Computer Science, Brown University
published: Aug. 26, 2009,   recorded: June 2009,   views: 3347


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We present skill bootstrapping, a proposed new research direction for agent learning and planning that allows an agent to start with low-level primitive actions, and develop skills that can be used for higher-level planning. Skills are developed over the course of solving many different problems in a domain, using reinforcement learning techniques to complement the bene fits and disadvantages of heuristic-search planning. We describe the overall architecture of the proposed approach, discuss how it relates to other work, and give motivating examples for why this approach would be successful.

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