Temporal abstraction and task decomposition drastically reduce the search space for planning and control, and are fundamental to making complex tasks amenable to learning. In the context of reinforcement learning, temporal abstractions are studied within the paradigm of hierarchical reinforcement learning. © 2011 IEEE.
CITATION STYLE
Stulp, F., & Schaal, S. (2011). Hierarchical reinforcement learning with movement primitives. In IEEE-RAS International Conference on Humanoid Robots (pp. 231–238). https://doi.org/10.1109/Humanoids.2011.6100841
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