A Task-Agnostic Regularizer for Diverse Subpolicy Discovery in Hierarchical Reinforcement Learning
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DOI: 10.1109/tsmc.2022.3209070
Abstract: The automatic subpolicy discovery approach in hierarchical reinforcement learning (HRL) has recently achieved promising performance on sparse reward tasks. This accelerates transfer learning and unsupervised intelligent creatures while eliminating the domain-specific knowledge constraint. Most previously… read more here.
Keywords: reinforcement learning; hierarchical reinforcement; subpolicy; subpolicy discovery ... See more keywords