Definition
Hierarchical reinforcement learning (HRL) decomposes a reinforcement learningproblem into a hierarchy of subproblems or subtasks such that higher-level parent-tasks invoke lower-level child tasks as if they were primitive actions. A decomposition may have multiple levels of hierarchy. Some or all of the subproblems can themselves be reinforcement learning problems. When a parent-task is formulated as a reinforcement learning problem it is commonly formalized as a semi-Markov decision problem because its actions are child-tasks that persist for an extended period of time. The advantage of hierarchical decomposition is a reduction in computational complexity if the overall problem can be represented more compactly and reusable subtasks learned or provided independently. While the solution to a HRL problem is optimal given the constraints of the hierarchy there are no guarantees in general that the decomposed solution is an optimal solution to the original reinforcement...
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Hengst, B. (2017). Hierarchical Reinforcement Learning. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_363
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