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On Efficiency in Hierarchical Reinforcement Learning
Neural Information Processing Systems
Hierarchical Reinforcement Learning (HRL) approaches promise to provide more efficient solutions to sequential decision making problems, both in terms of statistical as well as computational efficiency. While this has been demonstrated empirically over time in a variety of tasks, theoretical results quantifying the benefits of such methods are still few and far between. In this paper, we discuss the kind of structure in a Markov decision process which gives rise to efficient HRL methods.dblp:conf/nips/WenPIBRS20 fatcat:h76ra6twd5ewtc5msasgxhqcai