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Hierarchical Reinforcement Learning: A Survey and Open Research Challenges
2022
Machine Learning and Knowledge Extraction
Reinforcement learning (RL) allows an agent to solve sequential decision-making problems by interacting with an environment in a trial-and-error fashion. When these environments are very complex, pure random exploration of possible solutions often fails, or is very sample inefficient, requiring an unreasonable amount of interaction with the environment. Hierarchical reinforcement learning (HRL) utilizes forms of temporal- and state-abstractions in order to tackle these challenges, while
doi:10.3390/make4010009
fatcat:emexhacqtvgdbelvbufusneira