Hierarchical Reinforcement Learning: A Survey

By Mostafa Al-Emran. 2015

66 citations recorded by the source corpus.

Reinforcement Learning (RL) has been an interesting research area in Machine Learning and AI. Hierarchical Reinforcement Learning (HRL) that decomposes the RL problem into sub-problems where solving each of which will be more powerful than solving the entire problem will be our concern in this paper. A review of the state-of-the-art of HRL has been investigated. Different HRL-based domains have been highlighted. Different problems in such different domains along with some proposed solutions have been addressed. It has been observed that HRL has not yet been surveyed in the current existing research; the reason that motivated us to work on this paper. Concluding remarks are presented. Some ideas have been emerged during the work on this research and have been proposed for pursuing a future research.

Subfields: Artificial Intelligence

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Citations in the corpus

RODE: Learning Roles to Decompose Multi-Agent Tasks (2020, background): “Single-agent HRL focuses on learning temporal decomposition of tasks, either by learning sub-goals (Nachum et al., 2018b;a; Levy et al., 2018; Ghosh et al., 2018; Sukhbaatar et al., 2018; Dwiel et al., 2019; Nair & Finn, 2019; Nasiriany et al., 2019; Dilokthanakul et al., 2019) or by discovering reu”
How to Reuse and Compose Knowledge for a Lifetime of Tasks: A Survey on Continual Learning and Functional Composition (2022, methodology): “We refer the reader to separate surveys for a deeper study of this line of work (Barto and Mahadevan, 2003; Al-Emran, 2015; Pateria et al., 2021).”