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Constructing skill trees
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<p>Constructing skill trees (CST) is a hierarchical <a href="/facts/Reinforcement_learning/NrgPPS0Q">reinforcement learning</a> algorithm which can build skill trees from a set of sample solution trajectories obtained from demonstration. CST uses an incremental MAP (<a href="/facts/Maximum_a_posteriori/Y5zHyqfX">maximum a posteriori</a>) change point detection algorithm to segment each demonstration trajectory into skills and integrate the results into a skill tree. CST was introduced by George Konidaris, Scott Kuindersma, <a href="/facts/Andrew_Barto/7HHfpJ8T">Andrew Barto</a> and <a href="/facts/Roderic_Grupen/sBDzxCXI">Roderic Grupen</a> in 2010. </p>