Macro-action discovery based on change point detection and boosting

We present a novel approach to automatic macroaction discovery and its application to a complex goal-planning task. The problem of macro-action discovery is framed as one of multiple change point detection and is addressed with the help of the Dynamic Programming Boosting algorithm. The procedure is then employed to solve a complex goal-planning problem which entails an avatar navigating a 3D environment. By using DPBoost to decompose the problem into a number of simpler ones, we are able to successfully address both the complexity and partial observability of the environment. © 2012 IEEE.


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Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012, 1, 574-577
Year:
2012
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 Record created 2013-07-29, last modified 2018-03-17

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